@article{nokey,

title = {Facial soft tissue changes in patients following rapid maxillary expansion in children: A systematic review},

author = {Nafisa Marium Molla and Raisa Queiroz Catunda and Karla Carpio Horta and Heesoo Oh and Manuel O. Lagravère},

url = {https://www.sciencedirect.com/science/article/pii/S1761722725001421?pes=vor&utm_source=scopus&getft_integrator=scopus},

doi = {https://doi.org/10.1016/j.ortho.2025.101107},

year  = {2026},

date = {2026-06-01},

journal = {International Orthodontics},

volume = {24},

issue = {2},

abstract = {Background: Non-surgical rapid maxillary expansion (RME) has been widely investigated regarding skeletal effects, although fewer studies have assessed the associated soft tissue changes, which is an important factor to consider, as orthodontic treatment aims to achieve occlusal harmony and an aesthetically pleasing soft tissue balance. This systematic review examines the existing literature on facial soft tissue changes following rapid maxillary expansion (RME) in children with a maxillary transverse deficiency. Material and methods: A systematic search was conducted up to July 2025 in four major databases, including Embase (via Ovid), Medline (via Ovid), PubMed, Cochrane Library, and Google Scholar. Studies focused on children receiving RME were included. The risk of bias was assessed using the Revised Cochrane risk-of-bias tool for randomized trials and the ROBINS-I tool for the Risk of Bias in Non-randomized Studies of Interventions. This review followed the protocols recommended by the Preferred Reporting Items for a Systematic Review and Meta-analysis (PRISMA) guidelines. Results: Of the 1379 articles found, 15 included PICOS-based inclusion criteria: five randomized controlled trials, four cohort studies, and six controlled clinical trials. Alar width (0.5 to 2.21 mm), alar base width (0.17 to 2.81 mm), and nasolabial angle (0.9 degrees) increased significantly after RME treatment. Five studies were found to have a low risk of bias, seven had a moderate risk of bias, one had a serious risk of bias, and two had some concerns. Conclusions: Our findings suggest that RME results in variable soft tissue facial changes over time. The findings resulted statistically significant for alar width, alar base width, and nasolabial angle, showing changes in the facial soft tissues after non-surgical rapid maxillary expansion, especially in the nasal area. These results, which are directly related to the anatomical effects of the rapid maxillary expansion, need careful consideration by clinicians as the skeletal and the consequent soft tissue changes may be influenced by different factors, i.e. patient age, appliance activation and follow-up duration.},

keywords = {Facial Soft Tissue, implant supported rapid maxillary expansion, malocclusion, nasal changes, Rapid Maxillary Expansion, tooth-bone-borne rapid maxillary expansion},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{nokey,

title = {Stability of transverse changes with clear aligners and hyrax expanders from the mixed to the permanent dentition},

author = {Andrew Tsai and Heeyeon Suh and Sandra Khong Tai and Marta Baird and Heesoo Oh },

doi = {https://doi.org/10.1016/j.ajodo.2026.01.017},

issn = {0889-5406},

year  = {2026},

date = {2026-03-04},

journal = {American Journal of Orthodontics and Dentofacial Orthopedics},

abstract = {This study evaluated the longitudinal stability of transverse changes in patients treated with the Invisalign First system (IFS; Align Technology Inc, Tempe, Ariz) and hyrax expanders during the mixed dentition, followed through the permanent dentition. The objective was to determine whether expansion achieved during the mixed dentition was maintained through the transition to the permanent dentition. This retrospective longitudinal study included 119 patients: 44 patients treated with IFS, 36 patients treated with hyrax, and 39 untreated controls. Arch width changes at the canines, premolars, and first molars were measured on digital study models at 3 time points: pre-phase 1 treatment (T1), post-phase 1 treatment (T2), and pre phase 2 evaluation (T3). Vertical skeletal changes were evaluated in a cephalogram subgroup. Statistical analyses included analysis of variance and linear mixed-effects models. Both IFS and hyrax groups showed greater maxillary arch width increases from T1 to T3 (IFS: 1.8-2.8 mm; hyrax: 2.2-3.7 mm) than controls (0.4-1.2 mm; P <0.0001). At T2, hyrax showed significantly greater interpremolar and intermolar expansion (4.1-4.8 mm) than IFS (1.9-2.8 mm), but more relapse from T2 to T3. The IFS group also showed increased mandibular arch width from T1 to T3 (1.9-2.9 mm) compared with controls (P<0.05). No significant vertical skeletal changes from T1 to T3 were observed. Both IFS and hyrax produced maxillary arch expansion that was maintained throughout the transition from mixed to permanent dentition. The IFS also showed stable mandibular arch expansion, supporting its use for mild crowding or transverse deficiencies without long-term vertical effects.},

keywords = {clear aligners, Hyrax expander, permanent dentition, Transverse},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Craniofacial growth percentile curves: A clinical tool from the Craniofacial Growth Consortium Study},

author = {Heesoo Oh and Kevin M. Middleton and Manish Valiathan and Dana L. Duren and Kieran P. McNulty and James A. McNamara Jr. and Mark Hans and Richard J. Sherwood},

doi = {https://doi.org/10.1016/j.ajodo.2025.11.026},

issn = {0889-5406},

year  = {2026},

date = {2026-03-04},

journal = {American Journal of Orthodontics and Dentofacial Orthopedics},

abstract = {This study aimed to develop new cephalometric standards incorporating samples from across North America, focusing on creating sex-specific percentile growth curves for craniofacial cephalometric measures using the extensive longitudinal data from the Craniofacial Growth Consortium Study. This study comprised 2100 subjects (1056 males and 1044 females) with 17,290 lateral cephalometric radiographs, spanning ages of 2.5-31.3 years. Twenty-four linear cephalometric measurements were calculated, including traits from the basicranium, maxilla, and mandible. For each measurement, multilevel nonlinear growth models were used to estimate growth milestones by sex, percentile growth curves were created, and a web-interface tool was developed to facilitate the practical application of these growth curves. Growth milestones, including age at peak growth velocity and peak growth velocity, were estimated for each sex using double logistic growth models. The timing of peak growth velocity varies across different regions of the craniofacial complex. Craniofacial growth percentile curves were created and cross-validated. A web interface tool was created to allow users to retrieve individual-specific percentile scores. The developed percentile growth curves and web-based tool offer a robust framework for clinicians to assess individual growth patterns, identify deviations from normative growth, and estimate future growth potential, supporting more personalized treatment planning and timing. },

keywords = {cephalometrics, clinical tool, craniofacial growth, growth modeling},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A semi-automated assessment tool for craniofacial landmarks in CBCT: InVivo7 software},

author = {Luísa Schubach da Costa Barreto and Bruno Moreira das Neves and Jonas Bianchi and Heesoo Oh and Klaus Barretto dos Santos Lopes Batista and Jose Augusto Mendes Miguel},

url = {https://www.sciencedirect.com/science/article/pii/S0300571225007353?pes=vor&utm_source=scopus&getft_integrator=scopus},

doi = {https://doi.org/10.1016/j.jdent.2025.106292},

year  = {2026},

date = {2026-02-01},

urldate = {2026-02-01},

journal = {Journal of Dentistry},

volume = {165},

abstract = {Objectives: This study describes and evaluates the functionality of the InVivo7 3D imaging software as a semiautomated tool for identifying craniofacial landmarks in CBCT scans. Methods: AI-assisted landmark tracing in InVivo7 was used to automatically identify anatomical points in CBCT images. Each landmark was manually verified by a skilled evaluator to ensure accurate and reliable results, particularly for soft tissue markers and dental measurements, which often presented challenges for AI detection. The study utilized a standardized cephalometric analysis to compare the software’s performance. The evaluation included assessing the software’s ability to recognize skeletal, dental, and soft tissue structures accurately. Results: The semi-automated AI-assisted algorithm showed high precision in landmark identification. Manual verification confirmed its reliability and allowed the creation of a customized automated configuration for orthodontic diagnosis and treatment outcome evaluation. Specific clinical measures, such as the facial plane angle and molar relationships, were calculated using established formulas, allowing the software to categorize molar relationship classes (Angle Class I, II, III). Conclusions: InVivo7 presents a reliable and efficient tool for craniofacial landmark analysis, enhancing diagnostic accuracy while reducing manual labor. However, ongoing validation and software updates are essential to fully optimize its clinical applicability and ensure consistent performance across diverse patient populations. Future developments should focus on refining AI algorithms to improve soft tissue landmark detection and expanding datasets to enhance the robustness of automated analyses. Clinical Relevance: Rule-based automated algorithm CBCT craniofacial landmark detection using InVivo7 provides accurate, reproducible measurements, reducing manual workload and enhancing orthodontic diagnostic efficiency. Its integration into clinical practice supports standardized assessments, streamlining treatment planning.},

keywords = {3d, anatomic landmarks, artificial intelligence, CBCT, orthodontics},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Airway changes in growing Class II patients treated with Herbst appliance: A randomized controlled trial (RCT) comparing dental and skeletal anchorage},

author = {Luísa Schubach da Costa Barreto and Bruno Moreira das Neves and Deise Caldas Kuhlman and Nathalia Barbosa Palomares and Felipe de Assis Ribeiro Carvalho and Heeyeon Suh and Jonas Bianchi and Heesoo Oh and Klaus Barretto Dos Santos Lopes Batista and José Augusto Mendes Miguel },

url = {https://pubmed.ncbi.nlm.nih.gov/41417024/},

doi = {https://doi.org/10.1080/08869634.2025.2601531},

year  = {2025},

date = {2025-12-19},

journal = {Cranio - Journal of Craniomandibular and Sleep Practice},

pages = {1-13},

abstract = {Objective: To evaluate changes in the oropharyngeal airway (OP) with Herbst appliances using either dental or skeletal anchorage (HDA vs. HSA) in growing patients with Class II division 1 malocclusion. Design, Setting, and Participants: Forty patients (12.6 ± 1.4 years) at the peak of pubertal growth were randomized (HDA= 20; HSA= 20), for 12 months. Outcomes: change in OP volume, airway length and cross-sectional areas (minimum, maximum, average), by CBCT. Results: Thirty-four patients completed the study (HDA= 19; HSA= 15); and intention-to-treat (ITT) analysis was applied. Within-group analysis revealed a significant improvement for minimum axial area (HDA, p =.0061). Between-group comparisons showed significantly higher mean values in the HSA group for volume (p =.0044), airway length (p =.004), maximum cross-sectional area (p =.0188), and average area (p =.0122). Conclusions: HSA and HDA increased OP dimensions in growing Class II patients. Although HSA did not demonstrate a statistically superior effect, the dimensional changes represent morphological adaptations. Long-term studies are required to determine respiratory benefits.},

keywords = {Airway Changes, Angle Class II, Cone-beam computed tomography (CBCT), malocclusion, orthodontic appliances},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Interpretable machine learning integrates multi-source biomarkers for osteoarthritis diagnosis and mechanistic insights: A temporomandibular joint model},

author = {Najla Al Turkestani and Lucia Cevidanes and Jonas Bianchi and James Sugai and Marcela Gurgel and Juan Prieto and Elizabeth Hatfield and Kristine Philips and Erika Benavides and Fabiana Soki and Yuji Mishina and Margherita Fontana and Arvind Rao and Hongtu Zhu and Tengfei Li},

url = {https://www.sciencedirect.com/science/article/pii/S1063458425011124},

doi = {https://doi.org/10.1016/j.joca.2025.08.002},

issn = {1063-4584},

year  = {2025},

date = {2025-12-01},

journal = {Osteoarthritis and Cartilage},

volume = {33},

issue = {12},

pages = {1522-1533},

abstract = {Objective: Osteoarthritis (OA) is a pan-joint degenerative disorder characterized by cartilage degradation and subchondral bone remodeling. The temporomandibular joint (TMJ) offers a unique model for early OA due to its anatomy and early-onset disease. Current diagnostics rely on late-stage changes, underscoring the need for biomarker integration. We hypothesized that machine learning (ML) combining imaging, molecular, and clinical data would improve diagnostic accuracy, and that SHapley Additive exPlanations (SHAP) would clarify key predictors and interactions, enhancing mechanistic understanding of disease heterogeneity. Design: A case-control study of 162 participants (81 TMJ OA and 81 age- and sex-matched controls) integrated clinical, high-resolution imaging (radiomics, trabecular architecture, joint space), and systemic/articular biomarkers (serum and saliva). Seventy-seven ML combinations were evaluated via nested 10-fold cross-validation. Results: The final ensemble model achieved strong diagnostic performance (AUC=0.828, 95% CI: 0.757–0.892). SHAP analysis revealed top predictors such as headache severity, trabecular thickening, restless sleep, muscle soreness, limited mouth opening and joint space narrowing. Mechanistic interactions captured early inflammatory, structural, and neurovascular changes, including radiomics-cartilage degradation links (e.g., condyle grey level nonuniformity with saliva CXCL-16), clinical-molecular associations (e.g., headaches with saliva VE-cadherin), and subchondral microstructure correlations (e.g., grey level nonuniformity with run length nonuniformity). Conclusions: This study presents a clinically useful, explainable AI model for OA diagnosis. Key predictors and cross-domain interactions improved accuracy and clarified early disease mechanisms. Although cross-validation minimized overfitting risk, external validation is needed. These findings support biomarker-driven precision diagnostics and highlight multi-tissue predictors as potential targets for early OA intervention.},

keywords = {Cartilage degeneration, Diagnostic biomarkers, osteoarthritis, Subchondral bone remodeling},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A comparative analysis of artificial intelligence generated 3d facial models from 2D photos: A procrustes-based validation of landmark accuracy in Orthodontics},

author = {Jonas Bianchi and Lorena Wilka and Gabriel Bravo Vallejo and Felicia Miranda and Camila Massaro and Lucia Cevidanes and Heesoo Oh},

url = {https://www.sciencedirect.com/science/article/pii/S1073874625001409?pes=vor&utm_source=scopus&getft_integrator=scopus},

doi = {https://doi.org/10.1053/j.sodo.2025.10.014},

issn = {1073-8746},

year  = {2025},

date = {2025-10-29},

journal = {Seminars in Orthodontics},

pages = {1-9},

abstract = {Objectives: The primary aim of this study was to evaluate the accuracy and precision of a deep learning artificial intelligence (AI) model in reconstructing 3D facial geometry from a single 2D photograph. The study specifically measured the coordinate differences of human-placed facial landmarks on the AI-generated models against those placed on 3D models from a gold-standard scanner (Bellus 3D). Secondary objectives included identifying dimensional variations (X, Y, Z) and systematic biases in the AI reconstructions. Methods: This was a retrospective comparative study using a sample of 47 anonymized patient records from the

University of the Pacific database. For each patient, a 3D facial model was generated from a 2D photograph using the 3DDFA-V2 deep learning model. These AI-generated models were compared to reference models captured on the same day using a Bellus 3D Scanner. Two trained observers identified a set of facial landmarks on both the AI generated and Bellus 3D models. The coordinate data were normalized using Procrustes analysis to compare shape and landmark position independent of scale and orientation. The differences in landmark coordinates between the two model types were then statistically analyzed. Results: The analysis revealed that human landmark identification on the AI-generated 3D models reconstructed from 2D photos showed consistently lower variability compared to the gold-standard scans, suggesting that those models may lead to more repeatable measurements. The greatest overall deviations (Euclidean error) were found in landmarks associated with the lateral and inferior borders of the face, such as Right Gonion (RGo), Trichion (Tr), and Left Gonion (LGo). Quantitatively, the mean Procrustes-normalized error across all landmarks was 0.19 ± 0.08 units (range: 0.08−0.45), corresponding approximately to 0.2 mm of proportional deviation when referenced to the 1:1 scale Bellus3D models. While the AI-generated model demonstrated high fidelity, systematic biases were observed mainly as inferior displacements of key landmarks, including Menton (−0.35 units) and Pogonion (−0.30 units), reflecting a consistent downward shift in AI-based reconstruction. Conclusions: The 3DDFA-V2 deep learning application demonstrated in this study is a promising low-cost tool for

generating 3D facial models from 2D photographs with a high degree of precision in landmark placement. While the AI models facilitated more repeatable landmarking, they also exhibited some systematic biases. These findings have clinical implications for the use AI-generated 3D models in diagnosis and treatment planning, highlighting areas where further refinement of the AI algorithm is needed.},

keywords = {3D facial imaging, artificial intelligence, facial landmarks, facial scanner, soft tissue morphology},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Nasoalveolar Molding in Unilateral Cleft Lip and Palate: A Three-Dimensional Analysis},

author = {Yoon Weon Suh and Yong Jong Park and Olivia Yue and Marilia Sayako Yatabe and Heesoo Oh and Lucia Cevidanes and Meghan Hansen and Sophia Dipietro and Jonas Bianchi},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ocr.70051?getft_integrator=scopus&utm_source=scopus&utm_source=scopus},

doi = {https://doi.org/10.1111/ocr.70051},

year  = {2025},

date = {2025-10-28},

journal = {Orthodontics and Craniofacial Research},

issue = {29},

pages = {83-92},

abstract = {Objectives: This study aimed to three-dimensionally analyze the effects of pre-surgical nasoalveolar molding (NAM) in patients with unilateral cleft lip and palate. Methods: The sample consisted of 66 patients (with pre- and post-NAM casts), collected from three centers. Landmarks and measurements were identified on each cast, and the changes from pre- to post-NAM were recorded and analyzed using a paired t-test. Results: Post-NAM, we observed a mean decrease of 5.18 mm in 3D cleft width, a 1.78 mm decrease in the sagittal cleft gap, a 1.34 mm decrease in the anterior arch width and a 2.27 mm increase in the posterior arch width. There was a greater inward rotation of the greater segment (8.50°) compared to the lesser segment (3.09°), an increase in arch depth (1.74 mm) and internal flexion (9.20°) of the lesser segment. No statistically significant changes in the greater segment's arch depth and internal flexion were observed. The morphological changes between pre- and post-NAM therapy were visualized by the closest-distance color maps and 3D superimposition assessments. Conclusions: This study demonstrated the significant morphological changes induced by pre-surgical nasoalveolar molding (NAM) in patients with unilateral cleft lip and palate through a three-dimensional analysis. There was a notable reduction in cleft width, inward rotation of the greater segment and changes in arch dimensions. },

keywords = {3D analysis, cleft lip and palate, Nasoalveolar, soft tissue morphology},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Anterior open bite treatment with clear aligners in adults},

author = {Heeyeon Suh and Bella Shen Garnett and Kimberly Mahood and Heesoo Oh},

url = {https://www.sciencedirect.com/science/article/pii/S1073874625001227?pes=vor&utm_source=scopus&getft_integrator=scopus},

doi = {https://doi.org/10.1053/j.sodo.2025.10.006},

issn = {1073-8746},

year  = {2025},

date = {2025-10-11},

journal = {Seminars in Orthodontics},

abstract = {Anterior open bites represent one of the most complex malocclusions to treat due to their multifactorial etiology and high risk of relapse. While traditionally managed with fixed appliances, extractions, temporary anchorage devices (TADs), or surgery, clear aligner therapy has emerged as an effective alternative for adult patients when biomechanics are properly applied. Anterior open bite correction with clear aligners occurs primarily through incisor extrusion, with limited and less predictable contributions from molar intrusion. This paper reviews current knowledge on anterior open bite correction with clear aligners, the predictability of relevant tooth movements, and clinical outcomes reported in the literature. It also presents a contemporary clinical protocol for managing anterior open bite with clear aligners, including setup strategies in the digital treatment plan, class-specific considerations, staging, and retention. },

keywords = {anterior open bite, biomechanics, clear aligners, Retention},

pubstate = {published},

tppubtype = {article}

}

@article{nokeyi,

title = {Factors influencing the predictive performance of artificial intelligence for craniofacial growth},

author = {Naeun Kwona and Jong-Hak Kima and Heeyeon Suh and Heesoo Oh and Shin-Jae Lee},

url = {https://angle-orthodontist.kglmeridian.com/view/journals/angl/96/1/article-p106.xml?isSearch=true},

doi = {10.2319/031025-197.1},

year  = {2025},

date = {2025-09-29},

urldate = {2025-09-29},

journal = {Angle Orthodontist},

volume = {96},

issue = {1},

pages = {106-113},

abstract = {Objectives: To evaluate factors influencing the prediction error of artificial intelligence (AI) that predict craniofacial growth and to identify an optimal AI training condition to improve the predictive performance of the AI model. Materials and Methods: Original growth data were collected from the Mathews longitudinal serial growth study. From the original data consisting of 1257 datasets from 33 growing children of northern European descent, 60 data subsets were generated using random resampling procedures to include 12, 18, and 24 subjects, with data sizes of 100, 200, 300, 400, and 500 datasets. The resampling procedures were repeated four times. Each subset was used to train and create a total of 60 AI models. The prediction accuracy of these models was evaluated using growth prediction errors at the lower lip landmark, labrale inferius, as a benchmark indicator. The prediction errors of the 60 AI models were analyzed according to the number of subjects and data sizes. Results: Prediction error decreased as the data size increased. However, increasing the number of subjects within the growth data led to higher prediction errors. Notably, the increase in prediction error caused by adding more subjects was more substantial than the improvement achieved by increasing the data size. Conclusions: The findings suggest that developing highly accurate AI-based craniofacial growth prediction models remains a significant challenge, even with extensive datasets.},

keywords = {artificial intelligence, craniofacial growth, data quantity, individual variability, prediction error},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Clear aligners for Class II correction in growing patients: elastics vs mandibular advancement},

author = {Esha Patel and Stacey H. Moon and Heeyeon Suh and James Chen and Sandra Khong Tai and Heesoo Oh},

url = {https://angle-orthodontist.kglmeridian.com/view/journals/angl/aop/article-10.2319-032825-251.1/article-10.2319-032825-251.1.xml?isSearch=true&body=FullText},

doi = {10.2319/032825-251.1},

year  = {2025},

date = {2025-09-25},

urldate = {2025-09-25},

journal = {Angle Orthodontist},

volume = {95},

abstract = {Objectives: To evaluate the skeletal and dental effects of Class II correction in growing patients using clear aligners, with either elastics or mandibular advancement (MA). Materials and Methods: The study included 66 growing Class II patients: 45 patients treated with clear aligners (20 using Class II elastics and 25 with MA) and 21 untreated controls observed over a comparable time period. Nine cephalometric and three study cast measurements were evaluated initially (T1) and the end of treatment (T2) to assess skeletal and dental changes. Results: The control group maintained a Class II molar relationship and overjet, whereas both treatment groups corrected to Class I. In the MA group, statistically significant skeletal changes from T1 to T2 were observed, including reduction in SNA (–1.09°) and ANB (–1.69°), in addition to dentialveolar Class II correction. The elastic group showed no statistically significant changes in SNA and ANB compared to the control group. Linear regression revealed 5.28° of lower incisor proclination with Class II elastics, whereas lower incisor inclination was maintained with MA treatment. Conclusions: Clear aligner treatment with Class II elastics and MA were effective for correcting Class II malocclusion in growing patients that would have otherwise been maintained without intervention. Although Class II correction was mainly due to dentoalveolar changes, a skeletal component was observed with MA treatment.},

keywords = {Class II, clear aligners, elastics, growing patients, Maxillomandibular advancement},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Enhancing airway obstruction diagnosis with multimodal 3D shape analysis},

author = {Lucie Dole and Claudia Trindade Mattos and Jonas Bianchi and Heesoo Oh and Karine Evangelista and José Valladares Neto and Sergio Luiz Mota-Júnior and Lucia Cevidanes and Juan Carlos Prieto},

url = {https://link.springer.com/content/pdf/10.1007/s11548-025-03527-6.pdf?utm_source=scopus&getft_integrator=scopus},

doi = {10.1007/s11548-025-03527-6},

year  = {2025},

date = {2025-09-23},

journal = {International Journal of Computer Assisted Radiology and Surgery},

abstract = {Purpose: Enlarged adenoids that obstruct nasal breathing can cause significant health complications, including cognitive deficits, cardiovascular risks, and developmental delays. Early and accurate diagnosis is critical for effective treatment planning, but current diagnostic methods—such as polysomnography and clinical visual inspection—are either time-consuming, expensive, or lack sufficient accuracy. As cone-beam computed tomography (CBCT) scans are frequently available for these patients and may complement diagnosis, we propose an open-source, automated deep learning tool for quantitative airway obstruction assessment. Our method leverages CBCT scans, which are automatically segmented and processed to extract 3D airway morphology. Methods: Our approach combines two advanced techniques for 3D shape analysis: multi-view and point cloud representations to capture both global and local airway features, enhancing classification and regression performance. Results: Our model achieves an accuracy of 81.88% in classifying the presence or absence of adenoid hypertrophy and demonstrates improved performance in predicting the nasopharynx airway obstruction ratio. While the model performs well in detecting severe cases, further refinement is needed to improve classification and regression across all severity levels. Conclusion: This tool has the potential to enhance clinical workflows by providing rapid, quantitative, and reproducible assessments of airway obstruction, offering a promising solution for improving diagnostic efficiency and patient outcomes in clinical practice.},

keywords = {airway obstruction, Classification, regression, shape analysis},

pubstate = {published},

tppubtype = {article}

}

@article{Molla2025,

title = {Comparison of soft tissue facial changes in patients 7–11 years of age with and without maxillary expansion utilizing CBCTs and 3D facial scans: A preliminary study},

author = {Nafisa Molla and Heesoo Oh and Giseon Heo and Raisa Catunda and Manuel Lagravère},

url = {https://www.sciencedirect.com/science/article/pii/S1761722725000336?via%3Dihub},

doi = {https://doi.org/10.1016/j.ortho.2025.100998},

year  = {2025},

date = {2025-09-01},

urldate = {2025-09-01},

journal = {International Orthodontics},

volume = {23},

issue = {3},

abstract = {Background: The objectives of this study are to evaluate the effects of maxillary expansion over a period of 12 months on facial soft tissue measurements in children aged 7–11 years with a maxillary transverse deficiency of at least 5 mm or bilateral posterior crossbite, utilizing both CBCTs and 3D facial scans, by comparison to a control group. Material and methods: Data was collected from 32 patients and consisted of two groups: control and treatment (Hyrax expansion via RME, 1 turn/day). Each patient in each group underwent CBCTs, 3D facial scans and hand-wrist radiographs at two time points: pre-treatment (T0), and after the completion of expansion at post-retention (T1, 12 months). CBCTs were assessed using 3D Slicer software and 3D facial scans were assessed using OrthoInsight 3D software. The soft tissue measurements evaluated included the following: alar width, alar base width, mouth width, philtrum width, nasal tip prominence, nasolabial angle, upper lip to E-line, lower lip to E-line, upper lip height, height of vermillion of upper lip, lower lip height, height of nose, lower facial height and intercanthal width. Statistical analysis included intra- and inter-rater variability, measurement error calculation and MANOVA tests. Results: From a total of 32 patients with two sets of imaging records, no statistically significant differences were found between the two groups over the one-year observation. However, when comparing the two modalities utilized in this study (CBCT imaging and 3D facial scanning), the correlation was not as optimal for specific outcome variables such as alar base width and intercanthal width, potentially due to anatomic, imaging protocols and patient related factors. Conclusion: The findings of this study suggest that the children in both groups experienced similar facial soft tissue changes.},

keywords = {3D Imaging, Facial Soft Tissue, malocclusion, Rapid Maxillary Expansion},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Orthodontics and bisphosphonates: Risks, limitations, and considerations: A narrative review},

author = {Lorena Wilka and Gabriel Bravo Vallejo and Felicia Miranda and Camila Massaro and Jose Fernando Castanha Henriques and Marcos Roberto de Freitas and Heesoo Oh and Jonas Bianchi},

url = {https://www.sciencedirect.com/science/article/pii/S1073874625000519

},

doi = {https://doi.org/10.1053/j.sodo.2025.06.001},

issn = {1073-8746},

year  = {2025},

date = {2025-09-01},

urldate = {2025-09-01},

journal = {Seminars in Orthodontics},

volume = {31},

issue = {4},

pages = {544-550},

abstract = {The interaction between pharmacological agents and orthodontic treatment is critical in clinical practice. Bisphosphonates have been shown to affect orthodontic tooth movement by altering bone metabolism, slowing bone remodeling, potentially prolonging treatment duration, and increasing the risk of bisphosphonate-related osteonecrosis of the jaw. This narrative review aims to synthesize current literature on the effects of bisphosphonates in orthodontics, highlighting potential complications and outlining key clinical considerations for orthodontists. Although understanding of bisphosphonates has progressed, evidence from human orthodontic studies remains limited and heterogeneous. As such, further high-quality research is necessary to establish definitive clinical guidelines. In the meantime, orthodontic care for patients on bisphosphonate therapy should be approached with caution, emphasizing individualized risk assessment and careful treatment planning to ensure patient safety and optimize outcomes.},

keywords = {Bisphosphonate, Orthodontic anchorage, Orthodontic Tooth Movement, orthodontics},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Treatment outcomes of 3D-printed custom and conventional mini-implant assisted rapid palatal expanders (MARPE)},

author = {Yash Sharma and Heeyeon Suh and Jonas Bianchi and Audrey Yoon and Heesoo Oh},

url = {https://link.springer.com/article/10.1186/s40510-025-00576-0?utm_source=getftr&utm_medium=getftr&utm_campaign=getftr_pilot&getft_integrator=scopus},

doi = {https://doi.org/10.1186/s40510-025-00576-0},

year  = {2025},

date = {2025-08-25},

urldate = {2025-08-25},

journal = {Progress in Orthodontics},

volume = {26},

number = {30},

issue = {1},

abstract = {Background: Maxillary expansion has been a treatment of choice for correcting transverse skeletal discrepancies, especially in growing patients. For older patients, Mini-implant Assisted Rapid Palatal Expansion (MARPE) offers a promising treatment option. This study evaluates the treatment outcomes of Custom 3D-printed MARPE compared to Conventional MARPE (MSE-II). Methods: This retrospective study analyzed CBCT images from 42 patients aged 16 to 35 years, comparing measurements before (T1) and after (T2) expansion. The conventional (n = 21) and custom (n = 21) MARPE groups were matched with age and sex. Skeletal and dental changes were evaluated measuring twelve distances and four angles using Dolphin Imaging Software (Chatsworth, Calif). The measurements included frontozygomatic and maxillary widths, nasal cavity width, and dentoalveolar inclination. The effectiveness of each appliance was evaluated based on magnitude of expansion and successful correction of transverse discrepancy. Results: The custom MARPE group demonstrated comparable or greater increase in width across various anatomical landmarks to the conventional group. Logistic regression suggested a trend toward higher odds of successful transverse discrepancy correction with custom MARPE. Conclusions: Custom 3D-printed MARPE appliances may offer advantages in achieving skeletal expansion in older patients. Individualized appliance design and strategic mini-implant placement could contribute to effective treatment. However, further research is needed to evaluate long-term outcomes, cost-effectiveness, and potential complications to better guide appliance selection for each patient. },

keywords = {3D Printed, Custom, dentoalveolar inclination, frontozygomatic, MARPE, retrospective},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Leveraging Generative Artificial Intelligence in Teaching, Scholarship and Dental Education: Use Cases and Reflections},

author = {Jonas Bianchi and Meixun Zheng},

url = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/ocr.12949?getft_integrator=scopus&utm_source=scopus},

doi = {10.1111/ocr.12949},

year  = {2025},

date = {2025-05-30},

urldate = {2025-05-30},

journal = {Orthodontics and Craniofacial Research},

pages = {1-8},

abstract = {The objective of this narrative review is to explore the role of generative artificial intelligence (genAI) in dental education, highlighting its emerging applications, potential benefits and implementation challenges. Since the launch of ChatGPT in 2022, genAI tools have gained traction in academic and clinical settings, enabling content generation, translation, summarisation, 

exam preparation and basic clinical planning. This review presents a series of illustrative use cases demonstrating how genAI has been integrated into teaching, research and clinical workflows in dental and orthodontic training. Each example underscores how AI can support faculty in course design, assist students with learning complex concepts and provide real time feedback for exam analysis and academic writing. However, the implementation of genAI is not without limitations. The review addresses common concerns, including misinformation, data privacy, fabricated references and ethical use in clinical contexts. Although the benefits of genAI are promising, this review emphasises the importance of human oversight and institutional policies to guide ethical and effective use. In conclusion, genAI offers valuable support in dental education when used responsibly. Continued dialogue among educators, students and policymakers is essential to ensure that AI tools are integrated thoughtfully 

and equitably into academic practice.},

keywords = {artificial intelligence, dental education, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Explainable artificial intelligence to quantify adenoid hypertrophy-related upper airway obstruction using 3D Shape Analysis},

author = {Claudia Trindade Mattos and Lucie Dole and Sergio Luiz Mota-Júnior and Adriana de Alcantara Cury-Saramago and Jonas Bianchi and Heesoo Oh and Karine Evangelista and José Valladares-Neto and Antonio Carlos de Oliveira Ruellas and Juan Carlos Prieto and Lucia Helena Soares Cevidanes },

url = {https://www.sciencedirect.com/science/article/pii/S0300571225001344},

doi = {https://doi.org/10.1016/j.jdent.2025.105689},

issn = {0300-5712},

year  = {2025},

date = {2025-05-01},

journal = {Journal of Dentistry},

volume = {156},

abstract = {Objectives: To develop and validate an explainable Artificial Intelligence (AI) model for classifying and quantifying upper airway obstruction related to adenoid hypertrophy using three-dimensional (3D) shape analysis of cone-beam computed tomography (CBCT) scans. Methods: 400 CBCT scans of patients aged 5–18 years were analyzed. Nasopharyngeal airway obstruction (NAO) ratio was calculated to label scans into four grades of obstruction severity, used as the ground truth. Upper airway surface meshes were used to train a deep learning model combining multiview and point-cloud approaches for 3D shape analysis and obstruction severity classification and quantification. Surface Gradient weighted Class Activation Mapping (SurfGradCAM) generated explainability heatmaps. Performance was evaluated using area under the curve (AUC), precision, recall, F1-score, mean absolute error, root mean squared error, and correlation coefficients. Results: The explainable AI model demonstrated strong performance in both classification and quantification tasks. The AUC values for the classification task ranged from 0.77 to 0.94, with the highest values of 0.88 and 0.94 for Grades 3 and 4, respectively, indicating excellent discriminative ability for identifying more severe cases of obstruction. The SurfGradCAM-generated heatmaps consistently highlighted the most relevant regions of the upper airway influencing the AI’s decision-making process. In the quantification task, the regression model successfully predicted the NAO ratio, with a strong correlation coefficient of 0.854 (p < 0.001) and R2 = 0.728, explaining a substantial proportion of the variance in NAO ratios. Conclusions: The proposed explainable AI model, using 3D shape analysis, demonstrated strong performance in classifying and quantifying adenoid hypertrophy-related upper airway obstruction in CBCT scans. Clinical significance: This AI model provides clinicians with a reliable, automated tool for standardized adenoid hypertrophy assessment. The model’s explainable nature enhances clinical confidence and patient communication, potentially improving diagnostic workflow and treatment planning.},

keywords = {3D shape analysis, Adenoid hypertrophy, artificial intelligence, CBCT, Upper Airway obstruction},

pubstate = {published},

tppubtype = {article}

}

@article{Jung2025,

title = {Accuracy and reliability of automated landmark identification and cephalometric measurements on cone beam computed tomography using Invivo software},

author = {Young-Eun Jung and Heeyeon Suh and Joorok Park and Heesoo Oh },

url = {https://angle-orthodontist.kglmeridian.com/view/journals/angl/95/4/article-p362.xml},

doi = {10.2319/122324-1049.1},

year  = {2025},

date = {2025-04-10},

urldate = {2025-04-10},

journal = {The Angle Orthodontist},

volume = {95},

issue = {4},

pages = {362-370},

abstract = {Objectives: To evaluate the accuracy and reliability of an automated landmark identification (ALI) system and the impact of ALI errors on cephalometric measurements on cone-beam computed tomography (CBCT) images. Materials and Methods: Thirty-one landmarks were identified on 76 CBCT images using Invivo7 software (Anatomage, San Jose, Calif). Ground truth was established by averaging landmark coordinates from two calibrated human examiners. The accuracy of the ALI system was assessed by the mean absolute error (MAE, mm) across coordinate axes, the mean error distance (mm), and the successful detection rate (SDR) for each landmark. Interexaminer reliability between the ALI and manual landmark location was evaluated. Eighteen cephalometric measurements were computed from 25 landmarks. Accuracy of measurements from the ALI system was assessed with the MAE and successful measurement rates (SMR). Results: The ALI system closely matched human examiners in landmark identification, with an average MAE of 0.94 +/- 0.99 mm. Across all three coordinate axes, 87% of the landmarks had <2 mm MAE. ALI average MAE for conventional linear and angular cephalometric measurements were 1.35 +/- 1.33 mm and 0.89 +/- 0.89 degrees, respectively. Only one measurement, Intercondylar Width, showed MAE >3 mm. Conclusions: The ALI system showed clinically acceptable accuracy and reliability for the majority of cephalometric landmarks and measurements. Clinicians are advised to critically evaluate ALI landmarks with substantial errors, to fully utilize the capabilities of commercial software effectively. (Angle Orthod. 2025;95:362–370.)},

keywords = {Automated, CBCT, Cephalometric analysis, Landmark Identification},

pubstate = {published},

tppubtype = {article}

}

@article{Oh2025,

title = {Screening sleep-disordered breathing (SDB) in the everyday dental office – Pediatric and adult patients},

author = {Heesoo Oh and Maryam Arab and Eugene Y Kim and Mark Vaughan and Joorok Park and Audrey Yoon},

url = {https://www.sciencedirect.com/science/article/pii/S1073874625000271?via%3Dihub},

doi = {https://doi.org/10.1053/j.sodo.2025.03.005},

year  = {2025},

date = {2025-03-24},

urldate = {2025-03-24},

journal = {Seminars in Orthodontics},

volume = {31},

abstract = {This paper discusses the growing role of dental professionals in screening for sleep-disordered breathing (SDB) in both pediatric and adult patients. It highlights etiology, risk factors, and clinical signs and symptoms, outlines effective screening methods, and emphasizes the importance of interdisciplinary collaboration in the management of SDB. By providing guidelines tailored to each age group, this paper aims to equip dental practitioners with the tools and knowledge to identify patients at risk for SDB, thereby supporting early intervention and improving patient outcomes.},

keywords = {Obstructive sleep apnea (OSA), Sleep questionnaire, Sleep-disordered breathing (SDB), Snoring},

pubstate = {published},

tppubtype = {article}

}

@article{Roseth2025,

title = {Comparison of individualized facial growth prediction models using artificial intelligence and partial least squares based on the Mathews growth collection},

author = {Jeffrey Roseth and Jong-Hak Kim and Jun-Ho Moon and Dong-Yub Ko and Heesoo Oh and Shin-Jae Lee and Heeyeon Suh},

url = {https://angle-orthodontist.kglmeridian.com/view/journals/angl/95/3/article-p249.xml},

doi = {10.2319/082124-687.1},

year  = {2025},

date = {2025-01-31},

journal = {The Angle Orthodontist},

volume = {95},

issue = {3},

pages = {249-258},

abstract = {Objectives: To develop facial growth prediction models using artificial intelligence (AI) under various conditions, and to compare performance of these models with each other as well as with the partial least squares (PLS) growth prediction model. Materials and Methods: Longitudinal lateral cephalograms from 33 subjects in the Mathews growth collection were utilized. A total of 1257 pairs of before and after growth lateral cephalograms were included. In each image, 46 hard and 32 soft tissue landmarks were manually identified. Growth prediction models were constructed using a deep learning method based on TabNet deep neural network and partial least squares (PLS) method. Prediction accuracies of the two methods were compared. Results: On average, artificial intelligence (AI) showed 0.61 mm less prediction error than PLS. Among the 77 predicted landmarks, AI was more accurate than PLS in 60 landmarks. When comparing AI models with varying numbers of training epochs, those with higher epochs yielded more accurate predictions. Overall, PLS and AI exhibited greater prediction errors for soft tissue and mandibular landmarks compared to hard tissue and maxillary landmarks. However, AI showed a smaller increase in prediction error in areas with greater variability. Conclusions: AI proved to be a valuable growth prediction method, with clinically acceptable prediction errors averaging 1.49 mm for 45 hard tissue landmarks and 1.71 mm for 32 soft tissue landmarks. PLS accurately predicted landmarks with low variability. However, AI generally outperformed PLS, particularly for landmarks in the lower part of the craniofacial structure and soft tissue, where uncertainty is considerable. (Angle Orthod. 2025;95:249–258.)},

keywords = {artificial intelligence, Growth prediction, longitudinal craniofacial growth records},

pubstate = {published},

tppubtype = {article}

}

@article{Kim2025,

title = {Craniofacial growth prediction models based on cephalometric landmarks in Korean and American children},

author = {Jong-Hak Kim and Jun-Ho Moon and Jeffrey Roseth and Heeyeon Suh and Heesoo Oh and Shin-Jae Lee},

url = {https://angle-orthodontist.kglmeridian.com/view/journals/angl/95/2/article-p219.xml},

doi = {10.2319/052324-399.1},

year  = {2025},

date = {2025-01-13},

journal = {The Angle Orthodontist},

volume = {95},

issue = {2},

pages = {219-226},

abstract = {Objectives: To compare differences in craniofacial growth prediction results for Korean and American children according to growth prediction models developed using Korean and American longitudinal growth data. Materials and Methods: Growth prediction models based on cephalometric landmarks were built for each population using longitudinally taken lateral cephalograms of Korean children and American children of northern European origin. The sample sizes of the serial datasets were 679 and 1257 for Korean and American children, respectively. On each cephalogram, 78 cephalometric landmarks were identified. The prediction models were based on the partial least squares method with 160 input and 154 output variables. For each group, growth was predicted by applying the prediction models developed using data from the same and different populations. The growth prediction results were compared and analyzed. Results: The growth prediction results obtained with the prediction model developed using data from the same population were more accurate (P<.0001). The results distinctively visualized the discrepancies in the growth prediction results if different population types were not considered. Conclusions: Applying a growth prediction model generated using data from the same population may be desirable. (Angle Orthod. 2025;95:219–226.)},

keywords = {artificial intelligence, Growth prediction, Longitudinal studies, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Deep Learning-Based Three-Dimensional Analysis Reveals Distinct Patterns of Condylar Remodelling After Orthognathic Surgery in Skeletal Class III Patients},

author = {Selene Barone and Lucia Cevidanes and Jonas Bianchi and Joao Roberto Goncalves and Amerigo Giudice},

url = {https://www.scopus.com/pages/publications/85214127594?origin=resultslist&source=sd-apx},

doi = {10.1111/ocr.12895},

year  = {2024},

date = {2024-12-24},

journal = {Orthodontics & Craniofacial Research},

issue = {28},

pages = {441-448},

abstract = {Objective: This retrospective study aimed to evaluate morphometric changes in mandibular condyles of patients with skeletal Class III malocclusion following two-jaw orthognathic surgery planned using virtual surgical planning (VSP) and analyzed with automated three-dimensional (3D) image analysis based on deep-learning techniques. Materials and Methods: Pre-operative (T1) and 12–18 months post-operative (T2) Cone-Beam Computed Tomography (CBCT) scans of 17 patients (mean age: 24.8 ± 3.5 years) were analyzed using 3DSlicer software. Deep-learning algorithms automated CBCT orientation, registration, bone segmentation, and landmark identification. By utilizing voxel-based superimposition of pre- and post-operative CBCT scans and shape correspondence, the overall changes in condylar morphology were assessed, with a focus on bone resorption and apposition at specific regions (superior, lateral and medial poles). The correlation between these modifications and the extent of actual condylar movements post-surgery was investigated. Statistical analysis was conducted with a significance level of α = 0.05. Results: Overall condylar remodeling was minimal, with mean changes of < 1 mm. Small but statistically significant bone resorption occurred at the condylar superior articular surface, while bone apposition was primarily observed at the lateral pole. The bone apposition at the lateral pole and resorption at the superior articular surface were significantly correlated with medial condylar displacement (p < 0.05). Conclusion: The automated 3D analysis revealed distinct patterns of condylar remodeling following orthognathic surgery in skeletal Class III patients, with minimal overall changes but significant regional variations. The correlation between condylar displacements and remodeling patterns highlights the need for precise pre-operative planning to optimize condylar positioning, potentially minimizing harmful remodeling and enhancing stability.},

keywords = {3D analysis, condylar morphology, condylar remodeling, Orthognathic Surgery, skeletal class III},

pubstate = {published},

tppubtype = {article}

}

@article{Kim2024,

title = {Craniofacial anatomical determinants of pediatric sleep-disordered breathing: A comprehensive review},

author = {Kyung-A Kim and Su-Jung Kim and Audrey Yoon},

url = {https://doi.org/10.1111/jopr.13984},

doi = {10.1111/jopr.13984},

issn = {1059-941X},

year  = {2024},

date = {2024-11-18},

journal = {Journal of Prosthodontics},

abstract = {Purpose: This narrative review aims to elucidate the anatomical features of sleep-disordered breathing (SDB) in children. By identifying key structures and intervening proactively, we seek to alter craniofacial growth patterns and improve functional outcomes for SDB children. Methods: The literature on pediatric sleep-disordered breathing (PSDB), pediatric obstructive sleep apnea (OSA), anatomical predispositions, and the relationship between skeletal deformity and PSDB was examined using PubMed and Google Scholar databases, covering studies from 2006 to 2024.Results: Forty relevant articles were reviewed, focusing on craniofacial characteristics associated with PSDB. The etiology of PSDB is multifactorial, with adenoid and palatal tonsil enlargement being the most common cause. While adenotonsillectomy is recommended as the primary treatment, residual SDB may result in craniofacial skeletal deformities contributing to upper airway constriction. Typical craniofacial phenotypes of SDB include excessive vertical growth and constriction of the maxilla, a retruded mandible, and posterior rotation, known as a Class II hyperdivergent pattern. Conversely, Class III with an underdeveloped maxilla shows a relatively lower risk for SDB due to reduced nasal cavity and nasopharyngeal airway volumes. Transverse maxillary constriction with a high, deep palatal vault is a significant risk factor. Additionally, nasal obstruction and low tongue posture, with or without a short lingual frenulum, are identified as craniofacial risk factors for SDB development in children. Conclusion: Early diagnosis and intervention are critical in managing PSDB. Dentists, through screening and early treatment, can significantly influence craniofacial growth and health outcomes. A multidisciplinary approach is essential for effective management, improving the quality of life and long-term health of affected children.},

keywords = {craniofacial traits, growth modification, mouth breathing, pediatric obstructive sleep apnea, pediatric sleep disordered breathing},

pubstate = {published},

tppubtype = {article}

}

@article{Bandao2024,

title = {Evaluation of force systems generated by Memory Titanol springs with different preactivation bends},

author = {Henrique Barcelos Bandao and Jonas Bianchi and Lucas Arrais de Campos and Alyssa Schiavon Gandini and Luiz Gonzaga Gandini Junior},

url = {https://doi.org/10.1590/2177-6709.29.5.e242430.oar},

doi = {10.1590/2177-6709.29.5.e242430.oar },

year  = {2024},

date = {2024-10-04},

urldate = {2024-10-04},

journal = {Dental Press Journal of Orthodontics},

volume = {29},

number = {5},

abstract = {Objective: This study evaluated the force system generated by the Memory Titanol® spring (MTS) with different preactivation bends using an orthodontic force tester (OFT). Methods: Three preactivations were tested using a 0.017 × 0.022-in stainless steel (SS) wire and a 0.018 × 0.025-in NiTi segment, with an activation of 30º in the posterior segment (β), with 0º (Group 1 [G1]), 45º (Group 2 [G2]), and 60º (Group 3 [G3]) in the anterior segment (α). Results:   The molars showed extrusion values of −1.33 N for G1 and −0.78 N for G2, and an intrusion value of 0.33 N for G3. The force in the premolars was intrusive with a variation of 1.34 N for G1 and 0.77 N for G2; and extrusive with a variation of −0.31 N for G3. Regarding the upright moment (Ty) of the molar, a distal moment was observed with values of 53.45 N.mm for G1 and 19.87 N.mm for G2, while G3 presented a mesial moment of −6.23 N.mm. G1, G2, and G3 all exhibited distal premolar moments (Ty) of 3.58, 2.45, and 0.68 N.mm, respectively. Conclusions: The tested preactivations exerted an extrusive force in G1 and G2 and an intrusive force in G3 during molar vertical movement. The premolar region in G1 and G2 showed intrusive force and distal moment.},

keywords = {Biomechanical phenomena, Corrective, orthodontics, tooth movement technique},

pubstate = {published},

tppubtype = {article}

}

@article{Leroux2024,

title = {Novel CBCT-MRI Registration Approach for Enhanced Analysis of Temporomandibular Degenerative Joint Disease},

author = {Gaelle Leroux and Claudia Mattos and Jeanne Claret and Eduardo Caleme and Selene Barone and Marcela Gurgel and Felicia Miranda and Joao Goncalves and Paulo Zupelari Goncalves and marina Morettin Zupelari and Larry Wolford and Nina Hsu and Antonio Ruellas and Jonas Bianchi and Juan Prieto and Lucia Cevidanes},

url = {https://doi.org/10.1007/978-3-031-73083-2_7},

doi = {10.1007/978-3-031-73083-2_7},

isbn = {978-3-031-73083-2},

year  = {2024},

date = {2024-09-29},

urldate = {2024-09-29},

journal = {Clinical Image-Based Procedures. CLIP},

volume = {15196},

pages = {63-72},

abstract = {Temporomandibular Degenerative Joint Disease (TM DJD) is characterized by chronic and progressive degeneration of the joint, leading to functional limitations. Converging on enhancing TM DJD diagnosis, prognosis, and personalized patient care, multimodal Cone Beam Computed Tomography (CBCT) and Magnetic Resonance Imaging (MRI) registration aims to allow comprehensive understanding of the articular disc and subchondral bone alterations towards elucidating the onset, advancement, and progression of TM DJDs. This study proposes a novel multimodal image registration (fusion) approach that combines image processing techniques with mutual information to register MRI to CBCT images, enhancing TMJ complex visualization and analysis. The algorithm leverages automated image orientation, resampling, MRI inversion, normalization and rigid mutual information registration methods to align and overlay multimodal images while preserving anatomical details. Evaluation on 70 CBCT and 70 MRI scans acquired at the same time points for 70 TM DJD patients demonstrates robustness to variations in image quality, anatomical morphology, and acquisition protocols. By integrating MRI soft tissue information with CBCT bony details, this novel open-source tool available in the 3D Slicer platform provides a more comprehensive patient-specific TM DJD model. The current 98.75% success rate, with mean absolute rotation differences of 1.53 degrees ± 1.75 degrees, 1.69 degrees ± 1.54 degrees, and 2.70 degrees ± 2.89 degrees in Pitch, Roll and Yaw respectively; and translation differences of 0.92mm ± 1.64mm, 0.98mm ± 0.85mm, and 0.5mm ± 0.43mm in respectively the Left-Right, Antero-Posterior and Supero-Inferior axes. The proposed approach has potential to enhance care for TM DJD and other conditions requiring multimodal images.

},

keywords = {3D Slicer, CBCT, MRI, TMJ complex visualization},

pubstate = {published},

tppubtype = {article}

}

@article{Moon2024,

title = {Establishing Occlusion for Cleft Lip and Palate Patient},

author = {Stacey H. Moon and Olivia Yue and Mark Vaughan and Heeyeon Suh and Heesoo Oh},

url = {https://scholar.kyobobook.co.kr/article/detail/4050069418395},

year  = {2024},

date = {2024-06-03},

journal = {Journal of the Korean Cleft Lip and Palate Society},

volume = {27},

number = {1},

pages = {6-17},

abstract = {Craniofacial cleft patients commonly present with under-developed maxilla, which may have a profound effect not only on their soft tissue facial appearance but also skeletal discrepancy and dental malocclusion. This case report presents the treatment of a 14-year-old girl with a Class III skeletal and dental malocclusion. Her occlusion is complicated by a maxillary anterior right osseous cleft, retrognathic narrow maxilla with associated anterior and posterior crossbite, dental crowding, retained primary teeth, and several missing permanent teeth including maxillary premolars, both permanent maxillary lateral incisors, and all third molars. Our treatment plan consisted of extractions of the remaining primary teeth, maxillary expansion, protraction of maxilla and substitution of the maxillary left premolar for a lateral incisor tooth. The need for orthognathic surgery was also explained to the patient prior to the start of the treatment. The treatment was successfully completed with fixed appliances, rapid palatal expander (RPE), facemask (FM), maxillary segmental advancement with the fistula closure, and alveolar bone graft and elastics. Septorhinoplasty was completed towards the end of the treatment. Orthodontic treatment was completed in 48 months. The maxillary anterior teeth were aesthetically restored after the orthodontic treatment was completed. The retention consisted of maxillary and mandibular Hawley retainers.},

keywords = {Class III, cleft lip, Dental Crowding, malocclusion, Rapid Palatal Expander (RPE)},

pubstate = {published},

tppubtype = {article}

}

@article{Torbaty2024,

title = {Vertical and transverse treatment effects of Invisalign First system compared to Hyrax maxillary expanders with fixed appliances in mixed dentition patients},

author = {Parisa Moravedje Torbaty and Heeyeon Suh and Sandra Khong Tai and Marta Baird and Robert Boyd and Heesoo Oh},

url = {https://doi.org/10.2319/121023-818.1},

doi = { 10.2319/121023-818.1},

issn = { 0003-3219},

year  = {2024},

date = {2024-05-17},

urldate = {2024-05-17},

journal = {Angle Orthodontist},

volume = {94},

issue = {5},

abstract = {Objectives: To compare vertical and transverse changes in mixed dentition patients treated with the Invisalign First System (IFS) to those treated with a banded hyrax expander with fixed appliances (Hyrax) and control groups, and to assess the efficiency rate of dental arch expansion with IFS. Materials and Methods: The study included 80 mixed dentition patients, with 40 in each group (IFS and Hyrax) and 40 controls from the American Association of Orthodontists Foundation Craniofacial Growth Legacy Collection. Skeletal and dental vertical dimension changes and arch width changes between pretreatment (T1) and posttreatment (T2) were evaluated. Results: Age at T1, time interval (T1-T2), sex, and Angle class did not significantly differ among the groups. Mandibular plane angle changes showed a similar reduction for the control and IFS groups, with no changes in the Hyrax group. However, the differences among the three groups did not reach statistical significance (P = .06). The Hyrax group showed significantly greater expansion in maxillary intermolar width compared to the IFS group, 4.4 vs 2.5 mm, respectively. The efficiency of maxillary expansion using IFS ranged from 52.3% to 76.87%.

Conclusions: During the mixed dentition stage, no significant changes occurred in vertical dimensions among the control, Hyrax, and IFS groups. Although there was a trend suggesting a greater reduction in mandibular plane angle in the IFS group compared to the Hyrax group, this may not be clinically significant given the less than 1° difference. IFS can be a viable option for addressing mild arch width deficiencies, with a predictable increase in intermolar width of approximately 2.5 mm.},

keywords = {Hyrax expander, Invisalign, Maxillary expansion, Transverse, Vertical},

pubstate = {published},

tppubtype = {article}

}

@article{deOliveira2024,

title = {Artificial intelligence as a prediction tool for orthognathic surgery assessment},

author = {Pedro Henrique José de Oliveira and Tengfei Li and Haoyue Li and João Roberto Gonçalves and Ary Santos-Pinto and Luiz Gonzaga Gandini Junior and Lucia Soares Cevidanes and Claudia Toyama and Guilherme Paladini Feltrin and Antonio Augusto Campanha and Melchiades Alves de Oliveira Junior and Jonas Bianchi},

url = {https://doi.org/10.1111/ocr.12805},

doi = {10.1111/ocr.12805},

issn = {1601-6335},

year  = {2024},

date = {2024-04-21},

journal = {Orthodontics & Craniofacial Research},

volume = {27},

issue = {5},

pages = {785-794},

abstract = {Introduction: An ideal orthodontic treatment involves qualitative and quantitative measurements of dental and skeletal components to evaluate patients' discrepancies, such as facial, occlusal, and functional characteristics. Deciding between orthodontics and orthognathic surgery remains challenging, especially in borderline patients. Advances in technology are aiding clinical decisions in orthodontics. The increasing availability of data and the era of big data enable the use of artificial intelligence to guide clinicians' diagnoses. This study aims to test the capacity of different machine learning (ML) models to predict whether orthognathic surgery or orthodontics treatment is required, using soft and hard tissue cephalometric values. Methods: A total of 920 lateral radiographs from patients previously treated with either conventional orthodontics or in combination with orthognathic surgery were used, comprising n = 558 Class II and n = 362 Class III patients, respectively. Thirty-two measures were obtained from each cephalogram at the initial appointment. The subjects were randomly divided into training (n = 552), validation (n = 183), and test (n = 185) datasets, both as an entire sample and divided into Class II and Class III sub-groups. The extracted data were evaluated using 10 machine learning models and by a four-expert panel consisting of orthodontists (n = 2) and surgeons (n = 2). Results: The combined prediction of 10 models showed top-ranked performance in the testing dataset for accuracy, F1-score, and AUC (entire sample: 0.707, 0.706, 0.791; Class II: 0.759, 0.758, 0.824; Class III: 0.822, 0.807, 0.89). Conclusions: The proposed combined 10 ML approach model accurately predicted the need for orthognathic surgery, showing better performance in Class III patients.},

keywords = {artificial intelligence, Class II, Class III, orthodontics, Orthognathic Surgery},

pubstate = {published},

tppubtype = {article}

}

@article{Prieto2024,

title = {ShapeAXI: shape analysis explainability and interpretability},

author = {Juan Carlos Prieto and Felicia Miranda and Marcela Gurgel and Luc Anchling and Nathan Hutin and Selene Barone and Najla Al Turkestani and Aron Aliaga Del Castillo and Marilia Yatabe and Jonas Bianchi and Lucia Cevidanes},

url = {https://doi.org/10.1117/12.3007053},

doi = {10.1117/12.3007053},

year  = {2024},

date = {2024-04-02},

journal = {Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications},

volume = {12931},

abstract = {ShapeAXI represents a cutting-edge framework for shape analysis that leverages a multi-view approach, capturing 3D objects from diverse viewpoints and subsequently analyzing them via 2D Convolutional Neural Networks (CNNs). We implement an automatic N-fold cross-validation process and aggregate the results across all folds. This ensures insightful explainability heat-maps for each class across every shape, enhancing interpretability and contributing to a more nuanced understanding of the underlying phenomena. We demonstrate the versatility of ShapeAXI through two targeted classification experiments. The first experiment categorizes condyles into healthy and degenerative states. The second, more intricate experiment, engages with shapes extracted from CBCT scans of cleft patients, efficiently classifying them into four severity classes. This innovative application not only aligns with existing medical research but also opens new avenues for specialized cleft patient analysis, holding considerable promise for both scientific exploration and clinical practice. The rich insights derived from ShapeAXI’s explainability images reinforce existing knowledge and provide a platform for fresh discovery in the fields of condyle assessment and cleft patient severity classification. As a versatile and interpretative tool, ShapeAXI sets a new benchmark in 3D object interpretation and classification, and its groundbreaking approach hopes to make significant contributions to research and practical applications across various domains. ShapeAXI is available in our GitHub repository https://github.com/DCBIA-OrthoLab/ShapeAXI.},

keywords = {CBCT, cleft patients, Convolutional Neural Networks, ShapeAXI},

pubstate = {published},

tppubtype = {article}

}

@article{Kato2024,

title = {Is three-piece maxillary segmentation surgery a stable procedure?},

author = {Renata Mayumi Kato and João Roberto Gonçalves and Jaqueline Ignácio and Larry Wolford and Patricia Bicalho de Mello and Julianna Parizotto and Jonas Bianchi},

url = {https://doi.org/10.4041/kjod23.166},

doi = {10.4041/kjod23.166},

issn = {2005-372X},

year  = {2024},

date = {2024-03-08},

urldate = {2024-03-08},

journal = {The Korean Journal of Orthodontics },

volume = {54},

issue = {2},

pages = {128-135},

abstract = {Objective: The number of three-piece maxillary osteotomies has increased over the years; however, the literature remains controversial. The objective of this study was to evaluate the skeletal stability of this surgical modality compared with that of one-piece maxillary osteotomy. Methods: This retrospective cohort study included 39 individuals who underwent Le Fort I maxillary osteotomies and were divided into two groups: group 1 (three pieces, n = 22) and group 2 (one piece, n = 17). Three cone-beam computed tomography scans from each patient (T1, pre-surgical; T2, post-surgical; and T3, follow-up) were used to evaluate the three-dimensional skeletal changes. Results: The differences within groups were statistically significant only for group 1 in terms of surgical changes (T2-T1) with a mean difference in the canine region of 3.09 mm and the posterior region of 3.08 mm. No significant differences in surgical stability were identified between or within the groups. The mean values of the differences between groups were 0.05 mm (posterior region) and –0.39 mm (canine region). Conclusions: Our findings suggest that one- and three-piece maxillary osteotomies result in similar post-surgical skeletal stability.},

keywords = {Maxillary osteotomy, Surgical procedures, tomography},

pubstate = {published},

tppubtype = {article}

}

@article{Turkestani2024,

title = {A comprehensive patient-specific prediction model for temporomandibular joint osteoarthritis progression},

author = {Najla Al Turkestani and Tengfei Li and Jonas Bianchi and Marcela Gurgel and Juan Prieto and Hina Shah and Erika Benavides and Fabiana Soki and Yuji Mishina and Margherita Fontana and Arvind Rao and Hongtu Zhu and Lucia Cevidanes},

url = {https://doi.org/10.1073/pnas.2306132121},

doi = {10.1073/pnas.2306132121},

issn = {0027-8424},

year  = {2024},

date = {2024-02-12},

urldate = {2024-02-12},

journal = {PNAS: Proceedings of the National Academy of Sciences of the United States of America},

volume = {121},

number = {8},

abstract = {Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative disease characterized by chronic pain and impaired jaw function. The complexity of TMJ OA has hindered the development of prognostic tools, posing a significant challenge in timely, patient-specific management. Addressing this gap, our research employs a comprehensive, multidimensional approach to advance TMJ OA prognostication. We conducted a prospective study with 106 subjects, 74 of whom were followed up after 2 to 3 y of conservative treatment. Central to our methodology is the development of an innovative, open-source predictive modeling framework, the Ensemble via Hierarchical Predictions through Nested cross-validation tool (EHPN). This framework synergistically integrates 18 feature selection, statistical, and machine learning methods to yield an accuracy of 0.87, with an area under the ROC curve of 0.72 and an F1 score of 0.82. Our study, beyond technical advancements, emphasizes the global impact of TMJ OA, recognizing its unique demographic occurrence. We highlight key factors influencing TMJ OA progression. Using SHAP analysis, we identified personalized prognostic predictors: lower values of headache, lower back pain, restless sleep, condyle high gray level-GL-run emphasis, articular fossa GL nonuniformity, and long-run low GL emphasis; and higher values of superior joint space, mouth opening, saliva Vascular-endothelium-growth-factor, Matrix-metalloproteinase-7, serum Epithelial-neutrophil-activating-peptide, and age indicate recovery likelihood. Our multidimensional and multimodal EHPN tool enhances clinicians' decision-making, offering a transformative translational infrastructure. The EHPN model stands as a significant contribution to precision medicine, offering a paradigm shift in the management of temporomandibular disorders and potentially influencing broader applications in personalized healthcare.},

keywords = {Ensemble via Hierarchical Predictions through Nested cross-validation tool (EHPN), Temporomandibular joint osteoarthritis},

pubstate = {published},

tppubtype = {article}

}

@article{Cuenin2024,

title = {Caries detection and characterization in pediatric patients using iTero 5D near-infrared technology},

author = {Kyle Cuenin and James Chen and Sandra Khong Tai and David Lee and Geraldine Gerges and Heesoo Oh},

url = {https://doi.org/10.1016/j.ajodo.2023.06.026},

doi = {10.1016/j.ajodo.2023.06.026},

year  = {2024},

date = {2024-01-16},

urldate = {2024-01-16},

journal = {American Journal of Orthodontics and Dentofacial Orthopedics},

volume = {165},

issue = {1},

pages = {54-63},

abstract = {Introduction: Near-infrared imaging (NIRI) has been proposed as an alternative to radiographs and uses nonionizing radiation in the near-infrared spectrum to differentially scatter light off tooth surfaces and generate images allowing interproximal caries detection. The new iTero 5D Element Scanner (Align Technology, Santa Clara, Calif) has integrated NIRI capture and viewing technology but has not been specifically studied in a pediatric population. Therefore, this study aimed to assess clinicians’ abilities to detect and characterize caries in pediatric patients using this instrument. Methods: Bitewing (BW) radiographs and an intraoral scan were captured on 17 pediatric patients (344 surfaces were analyzed). Data were randomized and graded by 5 calibrated clinicians individually with 2 different rounds of grading. Results: The reliability of lesion characterization (i.e., grade) among examiners was poor to fair in both systems, whereas the reliability of caries detection was moderate. Both systems had a high specificity and low sensitivity. The reliability of the characterization of the combined dataset was moderate to substantial, whereas, for detection, it was substantial. Conclusions: When using either BW or NIRI analysis, reliability is relatively poor, and clinicians are more likely to correctly identify a healthy tooth surface when compared with a carious surface. There is a small difference in error rate between BW and NIRI systems that is not likely to be clinically significant. When NIRI and BW data are combined, clinician agreement for both lesion characterization and detection increases significantly.},

keywords = {Bitewing (BW), Caries Detection, iTero 5D, Pediatric patients},

pubstate = {published},

tppubtype = {article}

}

@article{Suh2023b,

title = {Anterior open bite correction via molar intrusion: Diagnosis, advantages, and complications},

author = {Choi YJ and Suh H and Park JJ and Park JH},

url = {https://pubmed.ncbi.nlm.nih.gov/38185583/},

doi = {10.1016/j.ejwf.2023.12.006},

year  = {2024},

date = {2024-01-06},

journal = {J World Fed Orthod},

volume = {S2212-4438},

issue = {23},

abstract = {Anterior open bite can be effectively treated nonsurgically via molar intrusion. This technique, involving the intrusion of posterior teeth using temporary skeletal anchorage devices, prompts counterclockwise rotation of the mandible. This rotation not only corrects anterior open bite but also contributes to a decrease in anterior facial height, improvements in lip incompetency, and forward movement of the chin. For successful outcomes, temporary skeletal anchorage devices, installed on both the buccal and palatal sides, must deliver equivalent intrusion force to the maxillary teeth. Treatment planning should consider factors such as skeletal discrepancies, vertical excess, incisor exposure, and configuration of the occlusal plane. Clinicians are advised to closely monitor periodontal changes and consider overcorrection to ensure lasting stability and maintenance of incisal overlap post-treatment.},

keywords = {anterior openbite, Molar intrusion, Temporary skeletal anchorage devices, Treatment planning, Vertical malocclusions},

pubstate = {published},

tppubtype = {article}

}

@article{Suh2023,

title = {Reliability statistics every orthodontist should know},

author = {Moon JH and Lee JM and Park JA and Suh H and Lee SJ},

url = {https://doi.org/10.1053/j.sodo.2023.12.004},

doi = {10.1053/j.sodo.2023.12.004},

issn = {1073-8746},

year  = {2023},

date = {2023-12-29},

journal = {Seminars in Orthodontics},

abstract = {It is essential to conduct a reliability examination even if the method was considered reliable in the past, as it may not be reliable in a new study conducted by different researchers using different materials. The current article highlights the importance of reliability examination in orthodontic studies and explains which assessment methods are more appropriate than others. Several fallacies in reporting and interpreting reliability are also discussed. In addition, the article presents examples of reliability examination for one-, two-, and three-dimensional data using graphic visualization in a tutorial format.},

keywords = {Bland-Altman plot, Confidence ellipse, Confidence interval, Reliability},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023n,

title = {Stability of skeletal class III surgical treatment. Affect of high condylectomy.},

author = {Kato RM and Bianchi J and Parizotto J and Wolford LM and Peixoto AP and Goncalves JR},

url = {https://www.researchgate.net/publication/376665721_Stability_of_skeletal_class_III_surgical_treatment_Affect_of_high_condylectomy},

doi = {10.55905/cuadv15n12-083},

year  = {2023},

date = {2023-12-21},

journal = {Cuadernos de Educación y Desarrollo},

pages = {16666-16681},

abstract = {Purpose: To evaluate the role of high condylectomy on surgical treatment stability of Class III patients with condylar hyperplasia. Materials and Methods: Twenty-four patients (9 females and 15 males) were paired according to gender, age, skeletal and dental characteristics in three groups. Group 1 (no condylar hyperplasia) underwent orthognathic surgery only, Group 2 (with unilateral or bilateral condylar hyperplasia) underwent high condylectomy, articular disc repositioning and orthognathic surgery in the same procedure and Group 3 (with unilateral or bilateral condylar hyperplasia) underwent orthognathic surgery only. Lateral cephalometric radiographs were selected at the immediately before surgery (T1), immediate after surgery (T2) and longest follow-up (T3). Results: Cephalometric comparison between the 3 groups showed no significant differences among the variables at the initial observation period (T1). During the observational period (T3-T2), patients in Group 3 showed significant relapse at SNB (mean = 2.18, sd = 1.39 degrees), ANB (mean = -2.68, sd = 2.24 degrees), SN.Pog (mean = -1.48, sd = 1.66 degrees), OJ (mean = -2.99, sd = 1.64 mm), OB (mean = 1.45, sd = 1.16 mm), Ar-Go (mean = 2.23, sd = 2.66 mm), Ar-Gn (mean = 3.76, sd = 1.48 mm), S-Gn (mean = 2.3, sd = 2.34 mm) and ANS-Me (mean = 2.06, sd = 2.2 mm) demonstrating that treatment adopted was insufficient for stable results within this Group. Groups 1 and 2 remained stable one year after surgery. Conclusions: Orthognathic surgery for correction of skeletal class III malocclusion is a stable procedure for patients without condylar growth abnormalities and for patients undergoing simultaneous high condylectomies and articular disc repositioning. Those patients with preoperative condylar hyperplasia who underwent double-jaw surgery and no TMJ intervention experienced significant relapse.},

keywords = {high condylectomy, skeletal class III, surgical treatment},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023l,

title = {Automated artificial intelligence‐based three‐dimensional comparison of orthodontic treatment outcomes with and without piezocision surgery.},

author = {Gurgel M and Alvarez M.A and Aristizabal J.F and Baquero B and Gillot M and Al Turkestani N and et al},

url = {https://pubmed.ncbi.nlm.nih.gov/38009409/},

doi = {10.1111/ocr.12737},

year  = {2023},

date = {2023-11-27},

journal = {Orthod Craniofac Res},

abstract = {Objective(s): This study aims to evaluate the influence of the piezocision surgery in the orthodontic biomechanics, as well as in the magnitude and direction of tooth movement in the mandibular arch using novel artificial intelligence (AI)-automated tools.



Materials and methods: Nineteen patients, who had piezocision performed in the lower arch at the beginning of treatment with the goal of accelerating tooth movement, were compared to 19 patients who did not receive piezocision. Cone beam computed tomography (CBCT) and intraoral scans (IOS) were acquired before and after orthodontic treatment. AI-automated dental tools were used to segment and locate landmarks in dental crowns from IOS and root canals from CBCT scans to quantify 3D tooth movement. Differences in mesial-distal, buccolingual, intrusion and extrusion linear movements, as well as tooth long axis angulation and rotation were compared.



Results: The treatment time for the control and experimental groups were 13.2 ± 5.06 and 13 ± 5.52 months respectively (P = .176). Overall, anterior and posterior tooth movement presented similar 3D linear and angular changes in the groups. The piezocision group demonstrated greater (P = .01) mesial long axis angulation of lower right first premolar (4.4 ± 6°) compared with control group (0.02 ± 4.9°), while the mesial rotation was significantly smaller (P = .008) in the experimental group (0.5 ± 7.8°) than in the control (8.5 ± 9.8°) considering the same tooth.



Conclusion: The open source-automated dental tools facilitated the clinicians' assessment of piezocision treatment outcomes. The piezocision surgery prior to the orthodontic treatment did not decrease the treatment time and did not influence in the orthodontic biomechanics, leading to similar tooth movements compared to conventional treatment.},

keywords = {computer-assisted, Cone-beam computed tomography (CBCT), Damon system, Dental long axis, Image processing, imaging, self-ligating braces, three-dimensional},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023m,

title = {Enhancing skeletal stability and Class III correction through active orthodontist engagement in virtual surgical planning: A voxel-based 3-dimensional analysis. },

author = {Barone S and Cevidanes L and Miranda F and Gurgel ML and Anchling L and Hutin N and et al},

url = {https://www.sciencedirect.com/science/article/pii/S0889540623005966?via%3Dihub},

doi = {10.1016/j.ajodo.2023.09.016 },

year  = {2023},

date = {2023-11-27},

journal = {American Journal of Orthodontics & Dentofacial Orthopedics},

volume = {5406},

issue = {23},

abstract = {Introduction

Skeletal stability after bimaxillary surgical correction of Class III malocclusion was investigated through a qualitative and quantitative analysis of the maxilla and the distal and proximal mandibular segments using a 3-dimensional voxel-based superimposition among virtual surgical predictions performed by the orthodontist in close communication with the maxillofacial surgeon and 12-18 months postoperative outcomes.

Methods

A comprehensive secondary data analysis was conducted on deidentified preoperative (1 month before surgery [T1]) and 12-18 months postoperative (midterm [T2]) cone-beam computed tomography scans, along with virtual surgical planning (VSP) data obtained by Dolphin Imaging software. The sample for the study consisted of 17 patients (mean age, 24.8 ± 3.5 years). Using 3D Slicer software, automated tools based on deep-learning approaches were used for cone-beam computed tomography orientation, registration, bone segmentation, and landmark identification. Colormaps were generated for qualitative analysis, whereas linear and angular differences between the planned (T1-VSP) and observed (T1-T2) outcomes were calculated for quantitative assessments. Statistical analysis was conducted with a significance level of α = 0.05.

Results

The midterm surgical outcomes revealed a slight but significantly less maxillary advancement compared with the planned position (mean difference, 1.84 ± 1.50 mm; P = 0.004). The repositioning of the mandibular distal segment was stable, with insignificant differences in linear (T1-VSP, 1.01 ± 3.66 mm; T1-T2, 0.32 ± 4.17 mm) and angular (T1-VSP, 1.53° ± 1.60°; T1-T2, 1.54° ± 1.50°) displacements (P >0.05). The proximal segments exhibited lateral displacement within 1.5° for both the mandibular right and left ramus at T1-VSP and T1-T2 (P >0.05).

Conclusions

The analysis of fully digital planned and surgically repositioned maxilla and mandible revealed excellent precision. In the midterm surgical outcomes of maxillary advancement, a minor deviation from the planned anterior movement was observed.},

keywords = {3D analysis, Class III correction, maxillofacial surgeon, skeletal stability},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023c,

title = {Automatic landmark identification in cone‐beam computed tomography. },

author = {Gillot M and Miranda F and Baquero B and Ruellas A and Gurgel M and Al Turkestani N and et al},

url = {https://pubmed.ncbi.nlm.nih.gov/36811276/},

doi = {10.1111/ocr.12642},

year  = {2023},

date = {2023-11-26},

journal = {Orthod Craniofac Res},

volume = {26},

issue = {4},

pages = {560-567},

abstract = {Objective: To present and validate an open-source fully automated landmark placement (ALICBCT) tool for cone-beam computed tomography scans.



Materials and methods: One hundred and forty-three large and medium field of view cone-beam computed tomography (CBCT) were used to train and test a novel approach, called ALICBCT that reformulates landmark detection as a classification problem through a virtual agent placed inside volumetric images. The landmark agents were trained to navigate in a multi-scale volumetric space to reach the estimated landmark position. The agent movements decision relies on a combination of DenseNet feature network and fully connected layers. For each CBCT, 32 ground truth landmark positions were identified by 2 clinician experts. After validation of the 32 landmarks, new models were trained to identify a total of 119 landmarks that are commonly used in clinical studies for the quantification of changes in bone morphology and tooth position.



Results: Our method achieved a high accuracy with an average of 1.54 ± 0.87 mm error for the 32 landmark positions with rare failures, taking an average of 4.2 second computation time to identify each landmark in one large 3D-CBCT scan using a conventional GPU.



Conclusion: The ALICBCT algorithm is a robust automatic identification tool that has been deployed for clinical and research use as an extension in the 3D Slicer platform allowing continuous updates for increased precision.},

keywords = {anatomic landmarks, fiducial markers, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{Oh2023,

title = {Cross-sectional data accurately model longitudinal growth in the craniofacial skeleton},

author = {Middleton K.M and Duren D.L and McNult K.P and Oh H and Valiathan M and Sherwood R

},

url = {https://doi.org/10.1038/s41598-023-46018-x},

doi = {10.1038/s41598-023-46018-x},

year  = {2023},

date = {2023-11-07},

urldate = {2023-11-07},

journal = {Sci Rep},

volume = {13},

issue = {19294},

abstract = {Dense, longitudinal sampling represents the ideal for studying biological growth. However, longitudinal samples are not typically possible, due to limits of time, prohibitive cost, or health concerns of repeat radiologic imaging. In contrast, cross-sectional samples have few such drawbacks, but it is not known how well estimates of growth milestones can be obtained from cross-sectional samples. The Craniofacial Growth Consortium Study (CGCS) contains longitudinal growth data for approximately 2000 individuals. Single samples from the CGCS for individuals representing cross-sectional data were used to test the ability to predict growth parameters in linear trait measurements separately by sex. Testing across a range of cross-sectional sample sizes from 5 to the full sample, we found that means from repeated samples were able to approximate growth rates determined from the full longitudinal CGCS sample, with mean absolute differences below 1 mm at cross-sectional sample sizes greater than ~ 200 individuals. Our results show that growth parameters and milestones can be accurately estimated from cross-sectional data compared to population-level estimates from complete longitudinal data, underscoring the utility of such datasets in growth modeling. This method can be applied to other forms of growth (e.g., stature) and to cases in which repeated radiographs are not feasible (e.g., cone-beam CT).},

keywords = {Cone-beam computed tomography (CBCT), craniofacial skeleton, cross-sectional data, longitudinal growth},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023,

title = {Automated Orientation and Registration of Cone-Beam Computed Tomography Scans.},

author = {Anchling L and Hutin N and Huang Y and Barone S and Roberts S and Miranda F and et al},

url = {https://doi.org/10.1007/978-3-031-45249-9_5},

doi = {10.1007/978-3-031-45249-9_5},

isbn = {978-3-031-45249-9},

year  = {2023},

date = {2023-10-09},

urldate = {2023-10-09},

journal = {Lecture Notes in Computer Science},

volume = {14242},

abstract = {Automated clinical decision support systems rely on accurate analysis of three-dimensional (3D) medical and dental images to assist clinicians in diagnosis, treatment planning, intervention, and assessment of growth and treatment effects. However, analyzing longitudinal 3D images requires standardized orientation and registration, which can be laborious and error-prone tasks dependent on structures of reference for registration. This paper proposes two novel tools to automatically perform the orientation and registration of 3D Cone-Beam Computed Tomography (CBCT) scans with high accuracy (<3 and <2 mm of angular and linear errors when compared to expert clinicians). These tools have undergone rigorous testing and are currently being evaluated by clinicians who utilize the 3D Slicer open-source platform. Our work aims to reduce the sources of error in the 3D medical image analysis workflow by automating these operations. These methods combine conventional image processing approaches and Artificial Intelligence (AI) based models trained and tested on de-identified CBCT volumetric images. Our results showed robust performance for standardized and reproducible image orientation and registration that provide a more complete understanding of individual patient facial growth and response to orthopedic treatment in less than 5 min.},

keywords = {3D CBCT scans, Deep Learning, Image processing, medical image registration, standardized orientation},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023d,

title = {Three-dimensional decision support system for treatment of canine impaction.},

author = {Keener DJ and de Oliveria Ruellas A.C and Aron Aliaga Del Castillo and Arriola-Guillen LE and Bianchi J and Oh H and et al},

url = {https://pubmed.ncbi.nlm.nih.gov/37037759/},

doi = {10.1016/j.ajodo.2023.02.016},

year  = {2023},

date = {2023-10-01},

journal = {Am J Orthod Dentofacial Orthop},

volume = {164},

issue = {4},

pages = {491-504},

abstract = {Introduction: This study aimed to develop a 3-dimensional (3D) characterization of the severity of maxillary impacted canines and to test the clinical performance of this characterization as a treatment decision support tool.



Methods: Cone-beam computed tomography images obtained from 83 patients with 120 impacted maxillary canines were included. Quantitative information on the canine 3D position and qualitative assessment of root damage of adjacent teeth were evaluated. A severity index was constructed on the basis of the quantitative findings. Clinical applicability was tested by comparing clinical diagnosis and treatment planning for conventional records vs the 3D characterization via a 2-part survey.



Results: The average quantitative assessments of impacted maxillary canine position were 6.4 ± 3.6 mm from the midsagittal plane, 11.6 ± 3.1 mm in height relative to the occlusal plane, 31.5° ± 18° of roll, and 48.8° ± 14.3° of pitch. The severity index ranged from 0-13 with a mean score of 4.5 ± 2.2. Overlap with adjacent teeth was the greatest contributor (33%) to the index. Bicortically impacted canines caused the most severe root damage. Cone-beam computed tomography was preferred for assessing root damage and overall severity, whereas conventional imaging was sufficient for height and angulation assessment. The 3D report was very important or important for evaluating root damage, canine position, overall severity, and overlap. The 3D report changed most of the decisions relating to biomechanics, patient education, and treatment time estimate. The decision of exposure and traction vs extraction was changed 22% of the time after the presentation of the 3D report.



Conclusions: The overlap with adjacent teeth frequently contributes the most to the severity index. The 3D report provided relevant clinical information regarding the canine position, damage to adjacent teeth, and the severity index, with a profound impact on the decisions of the clinicians regarding biomechanics, patient education, and treatment time estimate.},

keywords = {canine impaction, Cone-beam computed tomography (CBCT), three-dimensional},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023j,

title = {Interpretable artificial intelligence for classification of alveolar bone defect in patients with cleft lip and palate. },

author = {Miranda F and Choudhari V and Barone S and Anchling L and Hutin N and Gurgel M and et al},

url = {https://doi.org/10.1038/s41598-023-43125-7},

doi = {10.1038/s41598-023-43125-7},

year  = {2023},

date = {2023-09-22},

journal = {Scientific Reports},

volume = {15861},

abstract = {Cleft lip and/or palate (CLP) is the most common congenital craniofacial anomaly and requires bone grafting of the alveolar cleft. This study aimed to develop a novel classification algorithm to assess the severity of alveolar bone defects in patients with CLP using three-dimensional (3D) surface models and to demonstrate through an interpretable artificial intelligence (AI)-based algorithm the decisions provided by the classifier. Cone-beam computed tomography scans of 194 patients with CLP were used to train and test the performance of an automatic classification of the severity of alveolar bone defect. The shape, height, and width of the alveolar bone defect were assessed in automatically segmented maxillary 3D surface models to determine the ground truth classification index of its severity. The novel classifier algorithm renders the 3D surface models from different viewpoints and captures 2D image snapshots fed into a 2D Convolutional Neural Network. An interpretable AI algorithm was developed that uses features from each view and aggregated via Attention Layers to explain the classification. The precision, recall and F-1 score were 0.823, 0.816, and 0.817, respectively, with agreement ranging from 97.4 to 100% on the severity index within 1 group difference. The new classifier and interpretable AI algorithm presented satisfactory accuracy to classify the severity of alveolar bone defect morphology using 3D surface models of patients with CLP and graphically displaying the features that were considered during the deep learning model's classification decision.},

keywords = {3D landmark identification, alveolar bone defect, artificial intelligence, cleft lip, cleft lip and palate},

pubstate = {published},

tppubtype = {article}

}

@article{Oh2023b,

title = {Caries detection and characterization in pediatric patients using iTero 5D near-infrared technology. },

author = {K Cuenin and J Chen and S Tai and D Lee and G Gerges and H Oh},

url = {https://pubmed.ncbi.nlm.nih.gov/37702639/},

doi = {10.1016/j.ajodo.2023.06.026},

year  = {2023},

date = {2023-09-13},

journal = {Am J Orthod Dentofacial Orthop},

volume = {165},

pages = {54-63},

abstract = {Introduction: Near-infrared imaging (NIRI) has been proposed as an alternative to radiographs and uses nonionizing radiation in the near-infrared spectrum to differentially scatter light off tooth surfaces and generate images allowing interproximal caries detection. The new iTero 5D Element Scanner (Align Technology, Santa Clara, Calif) has integrated NIRI capture and viewing technology but has not been specifically studied in a pediatric population. Therefore, this study aimed to assess clinicians' abilities to detect and characterize caries in pediatric patients using this instrument.



Methods: Bitewing (BW) radiographs and an intraoral scan were captured on 17 pediatric patients (344 surfaces were analyzed). Data were randomized and graded by 5 calibrated clinicians individually with 2 different rounds of grading.



Results: The reliability of lesion characterization (ie, grade) among examiners was poor to fair in both systems, whereas the reliability of caries detection was moderate. Both systems had a high specificity and low sensitivity. The reliability of the characterization of the combined dataset was moderate to substantial, whereas, for detection, it was substantial.



Conclusions: When using either BW or NIRI analysis, reliability is relatively poor, and clinicians are more likely to correctly identify a healthy tooth surface when compared with a carious surface. There is a small difference in error rate between BW and NIRI systems that is not likely to be clinically significant. When NIRI and BW data are combined, clinician agreement for both lesion characterization and detection increases significantly.},

keywords = {5D, Bitewing (BW), infrared technology, Near-infrared imaging (NIRI), Pediatric patients},

pubstate = {published},

tppubtype = {article}

}

@article{Oh2023d,

title = {Short-term stability of anterior open bite treatment with clear aligners in adults},

author = {H Suh and B Garnett and K Mahood and R Boyd and H Oh },

url = {https://pubmed.ncbi.nlm.nih.gov/37552148/},

doi = {10.1016/j.ajodo.2023.05.026},

year  = {2023},

date = {2023-08-07},

journal = {Am J Orthod Dentofacial Orthop},

volume = {164},

issue = {6},

pages = {774-782},

abstract = {Introduction: This study aimed to examine the stability of anterior open bite (AOB) treatment with clear aligners.



Methods: This retrospective cohort study included 52 adult AOB patients (aged >18 years; 15 males, 37 females) who underwent nonextraction clear aligner treatment and were at least 1 year posttreatment. Eleven cephalometric measurements were evaluated at pretreatment, end of active treatment, and at least 1-year posttreatment. Overbite change, the primary outcome variable, and other cephalometric changes during treatment and retention were calculated, and repeated measures analysis of variance were performed. Stepwise multiple regression was used to make a prediction equation for open bite relapse.



Results: The mean retention period was 2.1 ± 1.1 years. The mean change in overbite during treatment was 3.3 ± 1.5 mm; 6% of patients presented relapse at least 1 year after treatment completion. The mean change of overbite (0.2 ± 0.5 mm) during the retention period was not statistically significant (P = 0.59). None of the 11 cephalometric measurements showed significant change during the retention period. The prediction model showed that only the coefficient for a tongue posture issue at the initial examination was statistically significant.



Conclusions: AOB was successfully corrected in all 52 patients using only clear aligners with no additional adjunctive aids such as microimplants. When retained with maxillary and mandibular fixed retainers and maxillary and mandibular vacuum-formed retainers, there was no significant change in cephalometric measurements during the short-term retention period.},

keywords = {anterior openbite, clear aligners, short-term stability},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023h,

title = {Three-dimensional comparison between the effects of mandibular advancement device and maxillomandibular advancement surgery on upper airway.},

author = {Gurgel M and Cevidanes L and Costa F and Pereira R and Cunali P and Bittencourt L and et al},

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10314553/},

doi = {10.1186/s12903-023-03125-5},

year  = {2023},

date = {2023-06-30},

journal = {BMC Oral Health},

volume = {23},

issue = {436},

abstract = {Background

The efficacy of mandibular advancement devices (MAD) and maxillomandibular advancement (MMA) in improving upper airway (UA) patency has been described as being comparable to continuous positive airway pressure (CPAP) outcomes. However, no previous study has compared MAD and MMA treatment outcomes for the upper airway enlargement. This study aimed to evaluate three-dimensionally the UA changes and mandibular rotation in patients after MAD compared to MMA.



Methods

The sample consisted of 17 patients with treated with MAD and 17 patients treated with MMA matched by weight, height, body mass index. Cone-beam computed tomography from before and after both treatments were used to measure total UA, superior/inferior oropharynx volume and surface area; and mandibular rotation.



Results

Both groups showed a significant increase in the superior oropharynx volume after the treatments (p = 0.003) and the MMA group showed greater increase (p = 0.010). No statistical difference was identified in the MAD group considering the inferior volume, while the MMA group showed a significantly gain (p = 0.010) and greater volume (p = 0.024). Both groups showed anterior mandibular displacement. However, the mandibular rotation were statistically different between the groups (p < 0.001). While the MAD group showed a clockwise rotation pattern (-3.97 ± 1.07 and − 4.08 ± 1.30), the MMA group demonstrated a counterclockwise (2.40 ± 3.43 and 3.41 ± 2.79). In the MAD group, the mandibular linear anterior displacement was correlated with superior [p = 0.002 (r=-0.697)] and inferior [p = 0.004 (r = 0.658)] oropharynx volume, suggesting that greater amounts of mandibular advancement are correlated to a decrease in the superior oropharynx and an increase in the inferior oropharynx. In the MMA group, the superior oropharynx volume was correlated to mandibular anteroposterior [p = 0.029 (r=-0.530)] and vertical displacement [p = 0.047 (r = 0.488)], indicating greater amounts of mandibular advancement may lead to a lowest gain in the superior oropharynx volume, while a great mandibular superior displacement is correlated with improvements in this region.



Conclusions

The MAD therapy led to a clockwise mandibular rotation, increasing the dimensions of the superior oropharynx; while a counterclockwise rotation with greater increases in all UA regions were showed in the MMA treatment.},

keywords = {Cone-beam computed tomography (CBCT), mandibular advancement device, Maxillomandibular advancement, Obstructive sleep apnea, Three-dimensional assessment, Upper airway},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023g,

title = {Artificial Intelligence Applications in Dentistry},

author = {Bianchi J},

url = {https://doi.org/10.1080/19424396.2023.2204566},

year  = {2023},

date = {2023-05-31},

journal = {Journal of the California Dental Association },

volume = {51},

issue = {1},

keywords = {artificial intelligence, CHAT-GPT, DALL-E AI system, dentistry, OpenAI},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023f,

title = {Artificial intelligence applications in orthodontics. },

author = {Miranda F and Barone S and Gillot M and Baquero B and Anchling L and Hutlin B and et al},

url = {https://doi.org/10.1080/19424396.2023.2195585},

year  = {2023},

date = {2023-04-13},

urldate = {2023-04-13},

journal = {Journal of the California Dental Association },

volume = {51},

issue = {1},

abstract = {Objective

This manuscript describes strategies for assessment of precision of several diagnostic artificial intelligence (AI) tools in orthodontics, available open-source image analysis platforms, as well as the use of three-dimensional (3D) surface models and superimpositions.



Results

The advances described in this manuscript present perspectives on the controversies of whether AI is smarter than clinicians and may replace human clinical decisions. A thorough orthodontic diagnosis requires comprehensive 3D analysis of the interrelationships among the dentition, craniofacial skeleton and soft tissues. Forecasts have indicated that 3D printing technology will provide more than 60% of all dental treatment needs by 2025, and orthodontic companies as well as remote monitoring companies are already using AI technology, being it essential that the clinicians are prepared and knowledgeable with the technology advances now available.



Conclusions

The AI applications in orthodontics rely on the implementation into diagnostic image records, data analysis for clinical practice and research applications. Continuous training and validation of the AI orthodontic image tools are essential for improving the performance and generalizability of these methods.},

keywords = {artificial intelligence, imaging, orthodontics, three-dimensional},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023b,

title = {Clear aligner mandibular advancement in growing patients with Class II malocclusion.},

author = {Gurgel ML and de Oliveria Ruellas A.C and Bianchi J and McNamara JA and Tai S and Franchi L and et al.},

url = {https://pubmed.ncbi.nlm.nih.gov/37636594/},

doi = {10.1016/j.xaor.2023.01.003},

year  = {2023},

date = {2023-04-03},

journal = {AJO DO Clin Companion},

volume = {3},

issue = {2},

pages = {93-109},

abstract = {Treatment effects occurring during Class II malocclusion treatment with the clear aligner mandibular advancement protocol were evaluated in two growing patients: one male (12 years, 3 months) and one female (11 years, 9 months). Both patients presented with full cusp Class II molar and canine relationships. Intraoral scans and cone-beam computed tomography were acquired before treatment and after mandibular advancement. Three-dimensional skeletal and dental long-axis changes were quantified, in which the dental long axis was determined by registering the dental crowns obtained from intraoral scans to the root canals in cone-beam computed tomography scans obtained at the same time points. Class II correction was achieved by a combination of mandibular skeletal and dental changes. A similar direction of skeletal and dental changes was observed in both patients, with downward and forward displacement of the mandible resulting from the growth of the mandibular condyle and ramus. Dental changes in both patients included mesialization of the mandibular posterior teeth with flaring of mandibular anterior teeth. In these two patients, clear aligner mandibular advancement was an effective treatment modality for Class II malocclusion correction with skeletal and dental effects and facial profile improvement.},

keywords = {Class II malocclusion, clear aligners, growing patients, three-dimensional},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023e,

title = {Integrative Risk Predictors of Temporomandibular Joint Osteoarthritis Progression.},

author = {Cai L and Al Turkestani N and Cevidanes L and Bianchi J and Gurgel M and Najarian K and et al},

url = {https://doi.org/10.1117/12.2651940},

doi = {10.1117/12.2651940},

year  = {2023},

date = {2023-04-03},

journal = {Semantic Scholar},

abstract = {In this paper we propose feature selection and machine learning approaches to identify a combination of features for risk prediction of Temporomandibular Joint (TMJ) disease progression. In a sample of 32 TMJ osteoarthritis and 38 controls, feature selection of 5 clinical comorbidities, 43 quantitative imaging, 28 biological features and was performed using Maximum Relevance Minimum Redundancy, Chi-Square and Least Absolute Shrinkage and Selection Operator (LASSO) and Recursive Feature Elimination. We compared the performance of learning using concave and convex kernels (LUCCK), Support Vector Machine (SVM) and Random Forest (RF) approaches to predict disease cure/improvement or persistence/worsening. We show that the SVM model using LASSO achieves area under the curve (AUC), sensitivity and precision of 0.92±0.08, 0.85±0.19 and 0.76 ±0.18, respectively. Baseline levels of headaches, lower back pain, restless sleep, muscle soreness, articular fossa bone surface/bone volume and trabecular separation, condylar High Gray Level Run Emphasis and Short Run High Gray Level Emphasis, saliva levels of 6Ckine, Osteoprotegerin (OPG) and Angiogenin, and serum levels of 6ckine and Brain Derived Neurotrophic Factor (BDNF) were the most frequently occurring features to predict more severe TMJ osteoarthritis prognosis.

},

keywords = {machine learning, temporomandibular joint},

pubstate = {published},

tppubtype = {article}

}