@article{Turkestani2021,

title = {Clinical decision support systems in orthodontics: A narrative review of data science approaches.},

author = {N Al Turkestani and J Bianchi and R Deleat-Besson and et al},

url = {https://onlinelibrary.wiley.com/doi/10.1111/ocr.12492},

doi = {10.1111/ocr.12492 },

year  = {2021},

date = {2021-05-11},

urldate = {2021-05-11},

journal = {Orthod Craniofac Res},

abstract = {Advancements in technology and data collection generated immense amounts of information from various sources such as health records, clinical examination, imaging, medical devices, as well as experimental and biological data. Proper management and analysis of these data via high-end computing solutions, artificial intelligence and machine learning approaches can assist in extracting meaningful information that enhances population health and well-being. Furthermore, the extracted knowledge can provide new avenues for modern healthcare delivery via clinical decision support systems. This manuscript presents a narrative review of data science approaches for clinical decision support systems in orthodontics. We describe the fundamental components of data science approaches including (a) Data collection, storage and management; (b) Data processing; (C) In-depth data analysis; and (d) Data communication. Then, we introduce a web-based data management platform, the Data Storage for Computation and Integration, for temporomandibular joint and dental clinical decision support systems. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Cong2021,

title = {Dental long axes using digital dental models compared to cone-beam computed tomography.},

author = {A Cong and C Massaro and J Bianchi and ACDO Ruellas and et al},

url = {https://onlinelibrary.wiley.com/doi/10.1111/ocr.12489},

doi = {10.1111/ocr.12489},

year  = {2021},

date = {2021-05-09},

urldate = {2021-05-09},

journal = {Orthod Cranifac Res},

abstract = {Standard methods of evaluating tooth long axes are not comparable (digital dental models [DDMs], panoramic and cephalometric radiographs) or expose patients to more radiation (cone-beam computed tomography [CBCT]). This study aimed to compare angular changes in tooth long axes using DDMs vs using CBCTs. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Parizotto2021,

title = {Craniofacial and airway morphology of individuals with oculoauriculovertebral spectrum.},

author = {JOL Parizotto and AP Peixoto and KT Borsato and J Bianchi and et al},

url = {https://pubmed.ncbi.nlm.nih.gov/33713375/},

doi = {10.1111/ocr.12483},

year  = {2021},

date = {2021-03-13},

urldate = {2021-03-13},

journal = {Orthodontics & Craniofacial Research},

abstract = {The objectives of this study were to characterize the craniofacial and airway morphology of oculo-auriculo-vertebral spectrum (OAVS) individuals using computed tomography (CT) examination.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Boubolo2021,

title = {FlyBy CNN: a 3D surface segmentation framework},

author = {Louis Boubolo and Maxime Dumont and Serge Brosset and Jonas Bianchi and Antonio Ruellas and Marcela Gurgel and Camila Massaro and Aron Aliaga Del Castillo and Marcos Ioshida and Marilia Yatabe and Erika Benavides and Hector Rios and Fabiana Soki and Gisele Neiva and Beatriz Paniagua and Lucia Cevidanes and Martin Styner and Juan Carlos Prieto},

url = {https://pubmed.ncbi.nlm.nih.gov/33758460/},

doi = {10.1117/12.2582205},

year  = {2021},

date = {2021-02-15},

journal = {Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115962B },

abstract = {In this paper, we present FlyBy CNN, a novel deep learning based approach for 3D shape segmentation. FlyByCNN consists of sampling the surface of the 3D object from different view points and extracting surface features such as the normal vectors. The generated 2D images are then analyzed via 2D convolutional neural networks such as RUNETs. We test our framework in a dental application for segmentation of intra-oral surfaces. The RUNET is trained for the segmentation task using image pairs of surface features and image labels as ground truth. The resulting labels from each segmented image are put back into the surface thanks to our sampling approach that generates 1-1 correspondence of image pixels and triangles in the surface model. The segmentation task achieved an accuracy of 0.9.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Brosset2021,

title = {"Web infrastructure for data management, storage and computation", Proc. SPIE 11600,},

author = {Serge Brosset and Maxime Dumont and Lucia Cevidanes and Reza Soroushmehr and Jonas Bianchi and Marcela L Gurgel and Romain Deleat-besson and Celia Le and Antonio Ruellas and Marilia Yatabe and Liliane Rosas and Joao Goncalves and Kayvan Najarian and Jonathan Gryak and Beatriz Paniagua and Martin Styner and Juan Carlos Prieto},

url = {https://www.researchgate.net/publication/349303820_Web_Infrastructure_for_Data_Management_Storage_and_Computation},

doi = {10.1117/12.2582283},

year  = {2021},

date = {2021-02-14},

journal = {Proc SPIE 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1166001N},

abstract = {The Data Storage for Computation and Integration (DSCI) proposes management innovations for web-based secure data storage, algorithms deployment, and task execution. Its architecture allows inclusion of plugins for upload, browsing, sharing, and task execution in remote computing grids. Here, we demonstrate the DSCI implementation and the deployment of Image processing tools (TMJSeg), machine learning algorithms (MandSeg, DentalModelSeg), and advanced statistical packages (Multivariate Functional Shape Data Analysis, MFSDA), with data transfer and task execution handled by the clusterpost plug-in. Due to its comprehensive web-based design, local software installation is no longer required. The DSCI aims to enable and maintain a distributed computing and collaboration environment across multi-site clinical centers for the data processing of multisource features such as clinical, biological markers, volumetric images, and 3D surface models, with particular emphasis on analytics for temporomandibular joint osteoarthritis (TMJ OA).},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Oh2022j,

title = {Estimating Craniofacial Growth Cessation: Comparison of Asymptote- and Rate-Based Methods. },

author = {Hardin AM and Knigge RP and Oh H},

url = {https://pubmed.ncbi.nlm.nih.gov/33998905/},

doi = {10.1177/10556656211002675},

year  = {2021},

date = {2021-02-01},

urldate = {2021-02-01},

journal = {Cleft Palate Craniofacial Journal },

volume = {59},

issue = {2},

pages = {230-238},

abstract = {Objective: To identify differences between asymptote- and rate-based methods for estimating age and size at growth cessation in linear craniofacial measurements.



Design: This is a retrospective, longitudinal study. Five linear measurements were collected from lateral cephalograms as part of the Craniofacial Growth Consortium Study (CGCS). Four estimates of growth cessation, including 2 asymptote- (GCasym, GCerr) and 2 rate-based (GCabs, GC10%) methods, from double logistic models of craniofacial growth were compared.



Participants: Cephalometric data from participants in 6 historic longitudinal growth studies were included in the CGCS. At least 1749 individuals (870 females, 879 males), unaffected by craniofacial anomalies, were included in all analyses. Individuals were represented by a median of 11 images between 2.5 and 31.3 years of age.



Results: GCasym consistently occurred before GCerr and GCabs consistently occurred before GC10% within the rate-based approaches. The ordering of the asymptote-based methods compared to the rate-based methods was not consistent across measurements or between males and females. Across the 5 measurements, age at growth cessation ranged from 13.56 (females, nasion-basion, GCasym) to 24.39 (males, sella-gonion, GCerr).



Conclusions: Adolescent growth cessation is an important milestone for treatment planning. Based on our findings, we recommend careful consideration of specific definitions of growth cessation in both clinical and research settings since the most appropriate estimation method may differ according to patients' needs. The different methods presented here provide useful estimates of growth cessation that can be applied to raw data and to a variety of statistical models of craniofacial growth.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2021b,

title = {Quantitative bone imaging biomarkers to diagnose temporomandibular joint osteoarthritis.},

author = {J Bianchi and J R Goncalves and A C de Oliveira Ruellas and L M Ashman and J-B Vimort and M Yatabe and B Paniagua and P Hernandez and E Benavides and F N Soki and M Loshida and L H S Cevidanes},

url = {https://pubmed.ncbi.nlm.nih.gov/32605824/},

doi = {10.1016/j.ijom.2020.04.018},

year  = {2021},

date = {2021-02-00},

journal = {International Journal of Oral and Maxillofacial Surgery},

volume = {50},

number = {2},

pages = {227-235},

abstract = {Bone degradation of the condylar surface is seen in temporomandibular joint osteoarthritis (TMJ OA); however, the initial changes occur in the subchondral bone. This cross-sectional study was performed to evaluate 23 subchondral bone imaging biomarkers for TMJ OA. The sample consisted of high-resolution cone beam computed tomography scans of 84 subjects, divided into two groups: TMJ OA (45 patients with TMJ OA) and control (39 asymptomatic subjects). Six regions of each mandibular condyle scan were extracted for computation of five bone morphometric and 18 grey-level texture-based variables. The groups were compared using the Mann-Whitney U-test, and the receiver operating characteristics (ROC) curve was determined for each variable that showed a statically significance difference. The results showed statistically significant differences in the subchondral bone microstructure in the lateral and central condylar regions between the control and TMJ OA groups (P< 0.05). The area under the ROC curve (AUC) for these variables was between 0.620 and 0.710. In conclusion, 13 imaging bone biomarkers presented an acceptable diagnostic performance for the diagnosis of TMJ OA, indicating that the texture and geometry of the subchondral bone microarchitecture may be useful for quantitative grading of the disease.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Parizotto2020,

title = {"Can palatal splint improve stability of segmental Le Fort I osteotomies?."},

author = {O L P Juliana and T B Karina and P P Adriano and J Bianchi and S C Daniel and R G Joao },

url = {https://pubmed.ncbi.nlm.nih.gov/32533749/},

doi = {10.1111/ocr.12399},

year  = {2020},

date = {2020-11-00},

urldate = {2020-11-00},

journal = {Orthodontics & Craniofacial Research},

volume = {23},

number = {4},

pages = {486-492},

abstract = {The purpose of this study was to evaluate the influence of a palatal splint on stability in multi-segment maxillary osteotomies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sherwood2020,

title = {Bayesian Approach to Longitudinal Craniofacial Growth: The Craniofacial Growth Consortium Study.},

author = {R Sherwood and H Oh and M Valiathan and K McNulty and D Duren and R Knigge and A Hardin and C Holzhauser and K Middleton},

url = {https://anatomypubs.onlinelibrary.wiley.com/doi/10.1002/ar.24520},

doi = {10.1002/ar.24520},

year  = {2020},

date = {2020-10-04},

urldate = {2020-10-04},

journal = {American Association of Orthodontists Foundation},

abstract = {Early in the 20th century, a series of studies were initiated across North America to investigate and characterize childhood growth. The Craniofacial Growth Consortium Study (CGCS) combines craniofacial records from six of those growth studies (15,407 lateral cephalograms from 1,913 individuals; 956 females, 957 males, primarily European descent). Standard cephalometric points collected from the six studies in the CGCS allows direct comparison of craniofacial growth patterns across six North American locations. Three assessors collected all cephalometric points and the coordinates were averaged for each point. Twelve measures were calculated from the averaged coordinates. We implemented a multilevel double logistic equation to estimate growth trajectories fitting each trait separately by sex. Using Bayesian inference, we fit three models for each trait with different random effects structures to compare differences in growth patterns among studies. The models successfully identified important growth milestones (e.g., age at peak growth velocity, age at cessation of growth) for most traits. In a small number of cases, these milestones could not be determined due to truncated age ranges for some studies and slow, steady growth in some measurements. Results demonstrate great similarity among the six growth studies regarding craniofacial growth milestone estimates and the overall shape of the growth curve. These similarities suggest minor variation among studies resulting from differences in protocol, sample, or possible geographic variation. The analyses presented support combining the studies into the CGCS without substantial concerns of bias. The CGCS, therefore, provides an unparalleled opportunity to examine craniofacial growth from childhood into adulthood.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Brosset2020,

title = {3D Auto-Segmentation of Mandibular Condyles},

author = {B Serge and D Maxime and J Bianchi and R Antonio and C Lucia and Y Marilia and G Joao and C Erika and S Fabiana and P Beatriz and P Juan and N Kayvan and G Jonathan and S Reza },

url = {https://pubmed.ncbi.nlm.nih.gov/33018219/},

doi = {10.1109/EMBC44109.2020.9175692},

year  = {2020},

date = {2020-07-00},

urldate = {2020-07-00},

journal = {2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)},

pages = {1270-1273},

abstract = {Temporomandibular joints (TMJ) like a hinge connect the jawbone to the skull. TMJ disorders could cause pain in the jaw joint and the muscles controlling jaw movement. However, the disease cannot be diagnosed until it becomes symptomatic. It has been shown that bone resorption at the condyle articular surface is already evident at initial diagnosis of TMJ Osteoarthritis (OA). Therefore, analyzing the bone structure will facilitate the disease diagnosis. The important step towards this analysis is the condyle segmentation. This article deals with a method to automatically segment the temporomandibular joint condyle out of cone beam CT (CBCT) scans. In the proposed method we denoise images and apply 3D active contour and morphological operations to segment the condyle. The experimental results show that the proposed method yields the Dice score of 0.9461 with the standards deviation of 0.0888 when it is applied on CBCT images of 95 patients. This segmentation will allow large datasets to be analyzed more efficiently towards data sciences and machine learning approaches for disease classification.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Currie2020,

title = {CBCT assessment of posterior cranial base and surrounding structures in orthodontically treated adolescents. Int Orthod.},

author = {Currie K and Oh H and Flores-Mir C and Lagravere M},

url = {https://pubmed.ncbi.nlm.nih.gov/32044297/},

doi = {10.1016/j.ortho.2020.01.004},

year  = {2020},

date = {2020-06-18},

urldate = {2020-06-18},

journal = {Int Orthod},

volume = {2},

pages = {266-275},

abstract = { Understanding craniofacial growth and development is important for accurate diagnosis, treatment planning and post-treatment evaluation of orthodontic cases. Paramount to this is knowledge of the cranial base growth and development, since it is the foundation upon which the remaining facial structures develop. The purpose of this study was to analyse different anatomical structures in the posterior cranial base and determine its changes during the adolescent years (13-15 year of age) using CBCT.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chen2020,

title = {The three-dimensional stable mandibular landmarks in patients between the ages of 12.5 and 17.1 years.},

author = {G Chen and M Al Awadi and D W Chambers and M O Lagravere-Vich and Y Xu and H Oh },

url = {https://pubmed.ncbi.nlm.nih.gov/32460733/},

doi = {10.1186/s12903-020-01142-2},

year  = {2020},

date = {2020-05-27},

urldate = {2020-05-27},

journal = {BMC Oral Health},

volume = {20},

number = {1},

pages = {153},

abstract = {With the aid of implants, Björk identified two-dimensional mandibular stable structures in cephalograms during facial growth. However, we do not know what the three-dimensional stable structures are with certainty. The purpose of this study was to identify the most stable mandibular landmarks in growing patients using three-dimensional images.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2020b,

title = {Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning.},

author = {Bianchi J and A C De Oliveira Ruellas and J R Goncalves and B Paniagua and J C Prieto and S Martin and Li Tengfei and Zhu Hongtu and S James and G William and B Erika and Soki Fabiana and Yatabe Marilia and Ashman Lawrence and W David and Soroushmehr Reza and N Kayvan and L H S Cevidanes },

url = {https://pubmed.ncbi.nlm.nih.gov/32415284/},

doi = {10.1038/s41598-020-64942-0},

year  = {2020},

date = {2020-05-15},

urldate = {2020-05-15},

journal = {Scientific Reports},

volume = {10},

number = {1},

pages = {8012},

abstract = {After chronic low back pain, Temporomandibular Joint (TMJ) disorders are the second most common musculoskeletal condition affecting 5 to 12% of the population, with an annual health cost estimated at $4 billion. Chronic disability in TMJ osteoarthritis (OA) increases with aging, and the main goal is to diagnosis before morphological degeneration occurs. Here, we address this challenge using advanced data science to capture, process and analyze 52 clinical, biological and high-resolution CBCT (radiomics) markers from TMJ OA patients and controls. We tested the diagnostic performance of four machine learning models: Logistic Regression, Random Forest, LightGBM, XGBoost. Headaches, Range of mouth opening without pain, Energy, Haralick Correlation, Entropy and interactions of TGF-β1 in Saliva and Headaches, VE-cadherin in Serum and Angiogenin in Saliva, VE-cadherin in Saliva and Headaches, PA1 in Saliva and Headaches, PA1 in Saliva and Range of mouth opening without pain; Gender and Muscle Soreness; Short Run Low Grey Level Emphasis and Headaches, Inverse Difference Moment and Trabecular Separation accurately diagnose early stages of this clinical condition. Our results show the XGBoost + LightGBM model with these features and interactions achieves the accuracy of 0.823, AUC 0.870, and F1-score 0.823 to diagnose the TMJ OA status. Thus, we expect to boost future studies into osteoarthritis patient-specific therapeutic interventions, and thereby improve the health of articular joints.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2020,

title = {Quantitative bone imaging biomarkers to diagnose temporomandibular joint osteoarthritis},

author = {J Bianchi and J R Goncalves and A C de Oliveira Ruellas and L M Ashman and J B Vimort and M Yatabe and B Paniagua and P Hernandez and E Benavides and F N Soki and M Loshida and L H S Cevidanes},

url = {https://www.sciencedirect.com/science/article/pii/S0901502720301636#:~:text=%20Quantitative%20bone%20imaging%20biomarkers%20to%20diagnose%20temporomandibular,This%20study%20followe...%204%20References.%20%20More%20},

doi = {0.1016/j.ijom.2020.04.018},

year  = {2020},

date = {2020-04-28},

urldate = {2020-04-28},

journal = {International Journal of Oral and Maxillofacial Surgery},

volume = {50},

number = {2},

pages = {227-235},

abstract = {Bone degradation of the condylar surface is seen in temporomandibular joint osteoarthritis (TMJ OA); however, the initial changes occur in the subchondral bone. This cross-sectional study was performed to evaluate 23 subchondral bone imaging biomarkers for TMJ OA. The sample consisted of high-resolution cone beam computed tomography scans of 84 subjects, divided into two groups: TMJ OA (45 patients with TMJ OA) and control (39 asymptomatic subjects). Six regions of each mandibular condyle scan were extracted for computation of five bone morphometric and 18 grey-level texture-based variables. The groups were compared using the Mann–Whitney U-test, and the receiver operating characteristics (ROC) curve was determined for each variable that showed a statically significance difference. The results showed statistically significant differences in the subchondral bone microstructure in the lateral and central condylar regions between the control and TMJ OA groups (P <  0.05). The area under the ROC curve (AUC) for these variables was between 0.620 and 0.710. In conclusion, 13 imaging bone biomarkers presented an acceptable diagnostic performance for the diagnosis of TMJ OA, indicating that the texture and geometry of the subchondral bone microarchitecture may be useful for quantitative grading of the disease.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hardin2020,

title = {Clinical implications of age-related change of the mandibular plane angle. },

author = {A Hardin and M Valiathan and H Oh and R Knigge and K McNulty and E Leary and D Duren and R Sherwood},

url = {https://pubmed.ncbi.nlm.nih.gov/31465622/},

doi = {10.1111/ocr.12342},

year  = {2020},

date = {2020-02-23},

urldate = {2020-02-23},

journal = {Orthod Craniofac Res},

volume = {1},

pages = {50-58},

abstract = {To identify trajectories of ontogenetic change in the mandibular plane angle (MPA) and to describe the influence of sex and other factors on MPA during growth.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Joy2020,

title = {Airway and cephalometric changes in adult orthodontic patients after premolar extractions.},

author = {A Joy and J Park and D Chambers and H Oh},

url = {https://pubmed.ncbi.nlm.nih.gov/31403835/#:~:text=Airway%20and%20cephalometric%20changes%20in%20adult%20orthodontic%20patients,measures%20in%20the%20nasopharynx%2C%20retropalatal%2C%20or%20retroglossal%20regions.},

doi = {10.2319/021019-92.1},

year  = {2020},

date = {2020-01-00},

urldate = {2020-01-00},

journal = {Angle Orthod},

volume = {90},

number = {1},

pages = {39-46},

abstract = {To examine changes in the airway and cephalometric measurements associated with orthodontic treatment of adults with and without premolar extractions. The study investigated whether extractions had a direct or indirect effect on the airway and examined selected skeletal and dental features.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Park2019,

title = {Reliability of 3D dental and skeletal landmarks on CBCT images.},

author = {Park J and S Baumrind and Curry S and Carlson S and H Oh},

url = {https://pubmed.ncbi.nlm.nih.gov/30883187/},

doi = {10.2319/082018-612.1},

year  = {2019},

date = {2019-09-00},

journal = {Angle Orthod},

volume = {89},

number = {5},

pages = {758-767},

abstract = {To quantify reliability of three-dimensional skeletal landmarks and a comprehensive set of dental landmarks in cone-beam computed tomography (CBCT) and to determine the shapes of envelope of error.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2019,

title = {Software comparison to analyze bone radiomics from high resolution CBCT scans of mandibular condyles.},

author = {Bianchi J and R C Joao and A C De Oliveira Ruellas and J B Vimort and Marilia Yatabe and P Beatriz and H Pablo and B Erika and N S Fabiana and S C Lucia Helena },

url = {https://pubmed.ncbi.nlm.nih.gov/31075043/},

doi = {10.1259/dmfr.20190049},

year  = {2019},

date = {2019-09-00},

urldate = {2019-09-00},

journal = {Dento Maxillo Facial Radiology},

volume = {48},

number = {6},

abstract = {Radiomics refers to the extraction and analysis of advanced quantitative imaging from medical images to diagnose and/or predict diseases. In the dentistry field, the bone data from mandibular condyles could be computationally analyzed using the voxel information provided by high-resolution CBCT scans to increase the diagnostic power of temporomandibular joint (TMJ) conditions. However, such quantitative information demands innovative computational software, algorithm implementation, and validation. Our study's aim was to compare a newly developed BoneTexture application to two-consolidated software with previous applications in the medical field, Ibex and BoneJ, to extract bone morphometric and textural features from mandibular condyles.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ryan2019,

title = {Cone-beam computed tomography airway measurements: Can we trust them?},

author = {P O R Daniel and Bianchi J and I Jaqueline and M W Larry and R G Joao },

url = {https://pubmed.ncbi.nlm.nih.gov/31256838/},

doi = {10.1016/j.ajodo.2018.07.024},

year  = {2019},

date = {2019-07-00},

urldate = {2019-07-00},

journal = {Am J Orthod Dentofacial Orthop},

volume = {156},

number = {1},

pages = {53-60},

abstract = {Pharyngeal airway space (PAS) assessment has been used in the past for a better understanding of orthodontic and surgical outcomes; however, this analysis could be unreliable. Our objective was to evaluate possible changes in the PAS reading in the same patient from their consecutive cone-beam computed tomography (CBCT) scans.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}