Publications
2021
Bianchi, J; Ruellas, A; Prieto, J C; Li, T; Soroushmehr, R; Najarian, K; Gryak, J; Deleat-Besson, R; Le, C; Yatabe, M; Gurgel, M; Turkestani, N A; Paniagua, B; Cevidanes, L
Decision support systems in temporomandibular Joint osteoarthritis: A review of data science and artificial intelligence applications. Journal Article
In: Seminars in Orthodontics, vol. 27, no. 2, pp. 78-86, 2021.
Abstract | Links | BibTeX | Tags: AAOF, Cone-beam computed tomography, Discrepency Index, malocclusion severity, mandibular asymmetry, orthodontic, Peer Assessment Rating Index, technique, vertical control, x-ray
@article{Bianchi2021,
title = {Decision support systems in temporomandibular Joint osteoarthritis: A review of data science and artificial intelligence applications.},
author = {J Bianchi and A Ruellas and J C Prieto and T Li and R Soroushmehr and K Najarian and J Gryak and R Deleat-Besson and C Le and M Yatabe and M Gurgel and N A Turkestani and B Paniagua and L Cevidanes},
url = {https://pubmed.ncbi.nlm.nih.gov/34305383/},
doi = {10.1053/j.sodo.2021.05.004},
year = {2021},
date = {2021-05-19},
urldate = {2021-05-19},
journal = {Seminars in Orthodontics},
volume = {27},
number = {2},
pages = {78-86},
abstract = {With the exponential growth of computational systems and increased patient data acquisition, dental research faces new challenges to manage a large quantity of information. For this reason, data science approaches are needed for the integrative diagnosis of multifactorial diseases, such as Temporomandibular joint (TMJ) Osteoarthritis (OA). The Data science spectrum includes data capture/acquisition, data processing with optimized web-based storage and management, data analytics involving in-depth statistical analysis, machine learning (ML) approaches, and data communication. Artificial intelligence (AI) plays a crucial role in this process. It consists of developing computational systems that can perform human intelligence tasks, such as disease diagnosis, using many features to help in the decision-making support. Patient's clinical parameters, imaging exams, and molecular data are used as the input in cross-validation tasks, and human annotation/diagnosis is also used as the gold standard to train computational learning models and automatic disease classifiers. This paper aims to review and describe AI and ML techniques to diagnose TMJ OA and data science approaches for imaging processing. We used a web-based system for multi-center data communication, algorithms integration, statistics deployment, and process the computational machine learning models. We successfully show AI and data-science applications using patients' data to improve the TMJ OA diagnosis decision-making towards personalized medicine.},
keywords = {AAOF, Cone-beam computed tomography, Discrepency Index, malocclusion severity, mandibular asymmetry, orthodontic, Peer Assessment Rating Index, technique, vertical control, x-ray},
pubstate = {published},
tppubtype = {article}
}
Turkestani, N Al; Bianchi, J; Deleat-Besson, R; et al,
Clinical decision support systems in orthodontics: A narrative review of data science approaches. Journal Article
In: Orthod Craniofac Res, 2021.
Abstract | Links | BibTeX | Tags: AAOF, clinical orthodontist, Cone-beam computed tomography, Cranial base, craniofacial, hyperdivergent, malocclusion severity, mandibular asymmetry, Posttreatment, technique
@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 = {AAOF, clinical orthodontist, Cone-beam computed tomography, Cranial base, craniofacial, hyperdivergent, malocclusion severity, mandibular asymmetry, Posttreatment, technique},
pubstate = {published},
tppubtype = {article}
}
Boubolo, Louis; Dumont, Maxime; Brosset, Serge; Bianchi, Jonas; Ruellas, Antonio; Gurgel, Marcela; Massaro, Camila; Castillo, Aron Aliaga Del; Ioshida, Marcos; Yatabe, Marilia; Benavides, Erika; Rios, Hector; Soki, Fabiana; Neiva, Gisele; Paniagua, Beatriz; Cevidanes, Lucia; Styner, Martin; Prieto, Juan Carlos
FlyBy CNN: a 3D surface segmentation framework Journal Article
In: Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115962B , 2021.
Abstract | Links | BibTeX | Tags: AAOF, Cone-beam computed tomography, Cranial base, Growth, hyperdivergent, mandibular asymmetry, Mandibular fixed retainer, Peer Assessment Rating Index, Posttreatment, pressure tension, technique, vertical control
@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 = {AAOF, Cone-beam computed tomography, Cranial base, Growth, hyperdivergent, mandibular asymmetry, Mandibular fixed retainer, Peer Assessment Rating Index, Posttreatment, pressure tension, technique, vertical control},
pubstate = {published},
tppubtype = {article}
}
2020
Serge, B; Maxime, D; Bianchi, J; Antonio, R; Lucia, C; Marilia, Y; Joao, G; Erika, C; Fabiana, S; Beatriz, P; Juan, P; Kayvan, N; Jonathan, G; Reza, S
3D Auto-Segmentation of Mandibular Condyles Journal Article
In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 1270-1273, 2020.
Abstract | Links | BibTeX | Tags: AAOF, Adolescents, anterior openbite, Cone-beam computed tomography, Cranial base, craniofacial, Discrepency Index, extraction, hyperdivergent, mandibular asymmetry, Mandibular fixed retainer, Mandibular remodeling, orthodontic, Peer Assessment Rating Index, Posttreatment, teaching
@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 = {AAOF, Adolescents, anterior openbite, Cone-beam computed tomography, Cranial base, craniofacial, Discrepency Index, extraction, hyperdivergent, mandibular asymmetry, Mandibular fixed retainer, Mandibular remodeling, orthodontic, Peer Assessment Rating Index, Posttreatment, teaching},
pubstate = {published},
tppubtype = {article}
}
Bianchi, J; Goncalves, J R; de Oliveira Ruellas, A C; Ashman, L M; Vimort, J B; Yatabe, M; Paniagua, B; Hernandez, P; Benavides, E; Soki, F N; Loshida, M; Cevidanes, L H S
Quantitative bone imaging biomarkers to diagnose temporomandibular joint osteoarthritis Journal Article
In: International Journal of Oral and Maxillofacial Surgery, vol. 50, no. 2, pp. 227-235, 2020.
Abstract | Links | BibTeX | Tags: AAOF, Cone-beam computed tomography, Cranial base, Growth, hyperdivergent, mandibular asymmetry, Mandibular fixed retainer, Mandibular remodeling, orthodontic, pressure tension, technique
@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 = {AAOF, Cone-beam computed tomography, Cranial base, Growth, hyperdivergent, mandibular asymmetry, Mandibular fixed retainer, Mandibular remodeling, orthodontic, pressure tension, technique},
pubstate = {published},
tppubtype = {article}
}
2019
Sam, A; Currie, K; Oh, H; Flores-Mir, C; Lagravere-Vich, M
Reliability of different 3D cephalometric landmarks in CBCT: A systematic review. Journal Article
In: Angle Orthod, vol. 89, no. 2, pp. 317-332, 2019.
Abstract | Links | BibTeX | Tags: AAOF, anterior openbite, Cone-beam computed tomography, Cranial base, hyperdivergent, mandibular asymmetry, Mandibular fixed retainer, Mandibular remodeling
@article{Sam2019,
title = {Reliability of different 3D cephalometric landmarks in CBCT: A systematic review.},
author = {A Sam and K Currie and H Oh and C Flores-Mir and M Lagravere-Vich},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8120873/},
doi = {10.2319/042018-302.1},
year = {2019},
date = {2019-03-00},
urldate = {2019-03-00},
journal = {Angle Orthod},
volume = {89},
number = {2},
pages = {317-332},
abstract = {Conventional two-dimensional (2D) cephalometric radiography is an integral part of orthodontic patient diagnosis and treatment planning. One must be mindful of its limitations as it indeed is a 2D representation of a vaster three-dimensional (3D) object. Issues with projection errors, landmark identification, and measurement inaccuracies impose significant limitations, which may now be overcome with the advent of cone-beam computed tomography (CBCT). A systematic review of the reliability of different 3D cephalometric landmarks in CBCT imaging was conducted.},
keywords = {AAOF, anterior openbite, Cone-beam computed tomography, Cranial base, hyperdivergent, mandibular asymmetry, Mandibular fixed retainer, Mandibular remodeling},
pubstate = {published},
tppubtype = {article}
}
Garnett, B; Mahod, K; Nguyen, M; Al-Khateeb, A; Liu, S; Boyd, R; Oh, H
Cephalometric comparison of adult anterior open bite treatment using clear aligners and fixed appliances. Journal Article
In: Angle Orthodontist, vol. 89, no. 1, pp. 3-9, 2019.
Abstract | Links | BibTeX | Tags: AAOF, Cone-beam computed tomography, Cranial base, craniofacial, Discrepency Index, extraction, fixed appliances, hyperdivergent, mandibular asymmetry, Mandibular fixed retainer, Mandibular remodeling, mapping, open bite, Peer Assessment Rating Index, Posttreatment, research, x-ray
@article{Garnett2019,
title = {Cephalometric comparison of adult anterior open bite treatment using clear aligners and fixed appliances.},
author = {B Garnett and K Mahod and M Nguyen and A Al-Khateeb and S Liu and R Boyd and H Oh},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8137127/},
doi = {10.2319/010418-4.1},
year = {2019},
date = {2019-01-00},
journal = {Angle Orthodontist},
volume = {89},
number = {1},
pages = {3-9},
abstract = {To compare fixed appliances and clear aligner therapy in correcting anterior open bite and in controlling the vertical dimension in adult patients with hyperdivergent skeletal patterns.},
keywords = {AAOF, Cone-beam computed tomography, Cranial base, craniofacial, Discrepency Index, extraction, fixed appliances, hyperdivergent, mandibular asymmetry, Mandibular fixed retainer, Mandibular remodeling, mapping, open bite, Peer Assessment Rating Index, Posttreatment, research, x-ray},
pubstate = {published},
tppubtype = {article}
}
2018
Liu, S; Oh, H; Chambers, D; Baumrind, S; Xu, T
Interpreting Weightings of the Peer Assessment Rating Index and the Discrepancy Index across Contexts on Chinese Patients. Journal Article
In: European Journal of Orthodontics, vol. 40, no. 2, pp. 157-163, 2018.
Abstract | Links | BibTeX | Tags: clear aligners, clinical orthodontist, Cone-beam computed tomography, Cranial base, Growth, hyperdivergent, malocclusion severity, mandibular asymmetry, Peer Assessment Rating Index, teaching, vertical control
@article{Liu2017b,
title = {Interpreting Weightings of the Peer Assessment Rating Index and the Discrepancy Index across Contexts on Chinese Patients.},
author = {S Liu and H Oh and D Chambers and S Baumrind and T Xu},
url = {https://pubmed.ncbi.nlm.nih.gov/28575327/},
doi = {10.1093/ejo/cjx043},
year = {2018},
date = {2018-04-06},
urldate = {2018-04-06},
journal = {European Journal of Orthodontics},
volume = {40},
number = {2},
pages = {157-163},
abstract = {Determine optimal weightings of Peer Assessment Rating (PAR) index and Discrepancy Index (DI) for malocclusion severity assessment in Chinese orthodontic patients.},
keywords = {clear aligners, clinical orthodontist, Cone-beam computed tomography, Cranial base, Growth, hyperdivergent, malocclusion severity, mandibular asymmetry, Peer Assessment Rating Index, teaching, vertical control},
pubstate = {published},
tppubtype = {article}
}
2017
Currie, K; Sawchuk, D; Saltaji, H; Oh, H; Flores-Mir, C; Lagravere-Vich, M
Posterior cranial base natural growth and development: A systematic review. Journal Article
In: Angle Orthodontist, vol. 87, no. 6, pp. 897-910, 2017.
Abstract | Links | BibTeX | Tags: AAOF, adult, Cranial base, extraction, fixed appliances, Growth, mandibular asymmetry, Mandibular fixed retainer, Posttreatment, pressure tension, research, retrospective, vertical control, x-ray
@article{Currie2017b,
title = {Posterior cranial base natural growth and development: A systematic review. },
author = {K Currie and D Sawchuk and H Saltaji and H Oh and C Flores-Mir and M Lagravere-Vich},
url = {https://pubmed.ncbi.nlm.nih.gov/28737426/},
doi = {10.2319/032717-218.1},
year = {2017},
date = {2017-11-00},
journal = {Angle Orthodontist},
volume = {87},
number = {6},
pages = {897-910},
abstract = {To provide a synthesis of the published studies evaluating the natural growth and development of the human posterior cranial base (S-Ba).},
keywords = {AAOF, adult, Cranial base, extraction, fixed appliances, Growth, mandibular asymmetry, Mandibular fixed retainer, Posttreatment, pressure tension, research, retrospective, vertical control, x-ray},
pubstate = {published},
tppubtype = {article}
}
Hwang, Hyeon-Shik; Oh, Min-Hee; Oh, Hee-Kyun; Oh, Heesoo
Surgery-first approach in correcting skeletal Class III malocclusion with mandibular asymmetry Journal Article
In: American Journal of Orthodontics and Dentofacial Orthopedics, vol. 152, no. 2, pp. 255-267, 2017.
Abstract | Links | BibTeX | Tags: AAOF, adult, Class III, Cranial base, Discrepency Index, hyperdivergent, mandibular asymmetry, Mandibular fixed retainer, Mandibular remodeling, mapping, open bite, orthodontic, surgery-first
@article{Hwang2017,
title = {Surgery-first approach in correcting skeletal Class III malocclusion with mandibular asymmetry},
author = {Hyeon-Shik Hwang and Min-Hee Oh and Hee-Kyun Oh and Heesoo Oh},
url = {http://162.214.24.32/~crilorg/wp-content/uploads/2018/11/Surgery-first-approach-in-correctingskeletal-CLass-III_AJODO-2017.pdf},
doi = {10.1016/j.ajodo.2014.10.040},
year = {2017},
date = {2017-08-01},
urldate = {2017-08-01},
journal = {American Journal of Orthodontics and Dentofacial Orthopedics},
volume = {152},
number = {2},
pages = {255-267},
abstract = {This case report describes a surgical orthodontic case that used the recently introduced surgery-first approach to correct a severe skeletal Class III malocclusion. A 19-year-old woman presented with severe mandibular prognathism and facial asymmetry; she had been waiting for growth completion in order to pursue surgical correction. After prediction of the postsurgical tooth movement and surgical simulation, 2-jaw surgery that included maxillary advancement and differential mandibular setback was performed using a surgery-first approach. Immediate facial improvement was achieved and postsurgical orthodontic treatment was efficiently carried out. The total treatment time was 16 months. The patient's facial appearance improved significantly and a stable surgical orthodontic outcome was obtained. (Am J Orthod Dentofacial Orthop 2017;152:255-67) },
keywords = {AAOF, adult, Class III, Cranial base, Discrepency Index, hyperdivergent, mandibular asymmetry, Mandibular fixed retainer, Mandibular remodeling, mapping, open bite, orthodontic, surgery-first},
pubstate = {published},
tppubtype = {article}
}
Bianchi, J; Ruellas, A; Prieto, J C; Li, T; Soroushmehr, R; Najarian, K; Gryak, J; Deleat-Besson, R; Le, C; Yatabe, M; Gurgel, M; Turkestani, N A; Paniagua, B; Cevidanes, L
Decision support systems in temporomandibular Joint osteoarthritis: A review of data science and artificial intelligence applications. Journal Article
In: Seminars in Orthodontics, vol. 27, no. 2, pp. 78-86, 2021.
@article{Bianchi2021,
title = {Decision support systems in temporomandibular Joint osteoarthritis: A review of data science and artificial intelligence applications.},
author = {J Bianchi and A Ruellas and J C Prieto and T Li and R Soroushmehr and K Najarian and J Gryak and R Deleat-Besson and C Le and M Yatabe and M Gurgel and N A Turkestani and B Paniagua and L Cevidanes},
url = {https://pubmed.ncbi.nlm.nih.gov/34305383/},
doi = {10.1053/j.sodo.2021.05.004},
year = {2021},
date = {2021-05-19},
urldate = {2021-05-19},
journal = {Seminars in Orthodontics},
volume = {27},
number = {2},
pages = {78-86},
abstract = {With the exponential growth of computational systems and increased patient data acquisition, dental research faces new challenges to manage a large quantity of information. For this reason, data science approaches are needed for the integrative diagnosis of multifactorial diseases, such as Temporomandibular joint (TMJ) Osteoarthritis (OA). The Data science spectrum includes data capture/acquisition, data processing with optimized web-based storage and management, data analytics involving in-depth statistical analysis, machine learning (ML) approaches, and data communication. Artificial intelligence (AI) plays a crucial role in this process. It consists of developing computational systems that can perform human intelligence tasks, such as disease diagnosis, using many features to help in the decision-making support. Patient's clinical parameters, imaging exams, and molecular data are used as the input in cross-validation tasks, and human annotation/diagnosis is also used as the gold standard to train computational learning models and automatic disease classifiers. This paper aims to review and describe AI and ML techniques to diagnose TMJ OA and data science approaches for imaging processing. We used a web-based system for multi-center data communication, algorithms integration, statistics deployment, and process the computational machine learning models. We successfully show AI and data-science applications using patients' data to improve the TMJ OA diagnosis decision-making towards personalized medicine.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Turkestani, N Al; Bianchi, J; Deleat-Besson, R; et al,
Clinical decision support systems in orthodontics: A narrative review of data science approaches. Journal Article
In: Orthod Craniofac Res, 2021.
@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}
}
Boubolo, Louis; Dumont, Maxime; Brosset, Serge; Bianchi, Jonas; Ruellas, Antonio; Gurgel, Marcela; Massaro, Camila; Castillo, Aron Aliaga Del; Ioshida, Marcos; Yatabe, Marilia; Benavides, Erika; Rios, Hector; Soki, Fabiana; Neiva, Gisele; Paniagua, Beatriz; Cevidanes, Lucia; Styner, Martin; Prieto, Juan Carlos
FlyBy CNN: a 3D surface segmentation framework Journal Article
In: Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115962B , 2021.
@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}
}
Serge, B; Maxime, D; Bianchi, J; Antonio, R; Lucia, C; Marilia, Y; Joao, G; Erika, C; Fabiana, S; Beatriz, P; Juan, P; Kayvan, N; Jonathan, G; Reza, S
3D Auto-Segmentation of Mandibular Condyles Journal Article
In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 1270-1273, 2020.
@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}
}
Bianchi, J; Goncalves, J R; de Oliveira Ruellas, A C; Ashman, L M; Vimort, J B; Yatabe, M; Paniagua, B; Hernandez, P; Benavides, E; Soki, F N; Loshida, M; Cevidanes, L H S
Quantitative bone imaging biomarkers to diagnose temporomandibular joint osteoarthritis Journal Article
In: International Journal of Oral and Maxillofacial Surgery, vol. 50, no. 2, pp. 227-235, 2020.
@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}
}
Sam, A; Currie, K; Oh, H; Flores-Mir, C; Lagravere-Vich, M
Reliability of different 3D cephalometric landmarks in CBCT: A systematic review. Journal Article
In: Angle Orthod, vol. 89, no. 2, pp. 317-332, 2019.
@article{Sam2019,
title = {Reliability of different 3D cephalometric landmarks in CBCT: A systematic review.},
author = {A Sam and K Currie and H Oh and C Flores-Mir and M Lagravere-Vich},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8120873/},
doi = {10.2319/042018-302.1},
year = {2019},
date = {2019-03-00},
urldate = {2019-03-00},
journal = {Angle Orthod},
volume = {89},
number = {2},
pages = {317-332},
abstract = {Conventional two-dimensional (2D) cephalometric radiography is an integral part of orthodontic patient diagnosis and treatment planning. One must be mindful of its limitations as it indeed is a 2D representation of a vaster three-dimensional (3D) object. Issues with projection errors, landmark identification, and measurement inaccuracies impose significant limitations, which may now be overcome with the advent of cone-beam computed tomography (CBCT). A systematic review of the reliability of different 3D cephalometric landmarks in CBCT imaging was conducted.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Garnett, B; Mahod, K; Nguyen, M; Al-Khateeb, A; Liu, S; Boyd, R; Oh, H
Cephalometric comparison of adult anterior open bite treatment using clear aligners and fixed appliances. Journal Article
In: Angle Orthodontist, vol. 89, no. 1, pp. 3-9, 2019.
@article{Garnett2019,
title = {Cephalometric comparison of adult anterior open bite treatment using clear aligners and fixed appliances.},
author = {B Garnett and K Mahod and M Nguyen and A Al-Khateeb and S Liu and R Boyd and H Oh},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8137127/},
doi = {10.2319/010418-4.1},
year = {2019},
date = {2019-01-00},
journal = {Angle Orthodontist},
volume = {89},
number = {1},
pages = {3-9},
abstract = {To compare fixed appliances and clear aligner therapy in correcting anterior open bite and in controlling the vertical dimension in adult patients with hyperdivergent skeletal patterns.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Liu, S; Oh, H; Chambers, D; Baumrind, S; Xu, T
Interpreting Weightings of the Peer Assessment Rating Index and the Discrepancy Index across Contexts on Chinese Patients. Journal Article
In: European Journal of Orthodontics, vol. 40, no. 2, pp. 157-163, 2018.
@article{Liu2017b,
title = {Interpreting Weightings of the Peer Assessment Rating Index and the Discrepancy Index across Contexts on Chinese Patients.},
author = {S Liu and H Oh and D Chambers and S Baumrind and T Xu},
url = {https://pubmed.ncbi.nlm.nih.gov/28575327/},
doi = {10.1093/ejo/cjx043},
year = {2018},
date = {2018-04-06},
urldate = {2018-04-06},
journal = {European Journal of Orthodontics},
volume = {40},
number = {2},
pages = {157-163},
abstract = {Determine optimal weightings of Peer Assessment Rating (PAR) index and Discrepancy Index (DI) for malocclusion severity assessment in Chinese orthodontic patients.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Currie, K; Sawchuk, D; Saltaji, H; Oh, H; Flores-Mir, C; Lagravere-Vich, M
Posterior cranial base natural growth and development: A systematic review. Journal Article
In: Angle Orthodontist, vol. 87, no. 6, pp. 897-910, 2017.
@article{Currie2017b,
title = {Posterior cranial base natural growth and development: A systematic review. },
author = {K Currie and D Sawchuk and H Saltaji and H Oh and C Flores-Mir and M Lagravere-Vich},
url = {https://pubmed.ncbi.nlm.nih.gov/28737426/},
doi = {10.2319/032717-218.1},
year = {2017},
date = {2017-11-00},
journal = {Angle Orthodontist},
volume = {87},
number = {6},
pages = {897-910},
abstract = {To provide a synthesis of the published studies evaluating the natural growth and development of the human posterior cranial base (S-Ba).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Hwang, Hyeon-Shik; Oh, Min-Hee; Oh, Hee-Kyun; Oh, Heesoo
Surgery-first approach in correcting skeletal Class III malocclusion with mandibular asymmetry Journal Article
In: American Journal of Orthodontics and Dentofacial Orthopedics, vol. 152, no. 2, pp. 255-267, 2017.
@article{Hwang2017,
title = {Surgery-first approach in correcting skeletal Class III malocclusion with mandibular asymmetry},
author = {Hyeon-Shik Hwang and Min-Hee Oh and Hee-Kyun Oh and Heesoo Oh},
url = {http://162.214.24.32/~crilorg/wp-content/uploads/2018/11/Surgery-first-approach-in-correctingskeletal-CLass-III_AJODO-2017.pdf},
doi = {10.1016/j.ajodo.2014.10.040},
year = {2017},
date = {2017-08-01},
urldate = {2017-08-01},
journal = {American Journal of Orthodontics and Dentofacial Orthopedics},
volume = {152},
number = {2},
pages = {255-267},
abstract = {This case report describes a surgical orthodontic case that used the recently introduced surgery-first approach to correct a severe skeletal Class III malocclusion. A 19-year-old woman presented with severe mandibular prognathism and facial asymmetry; she had been waiting for growth completion in order to pursue surgical correction. After prediction of the postsurgical tooth movement and surgical simulation, 2-jaw surgery that included maxillary advancement and differential mandibular setback was performed using a surgery-first approach. Immediate facial improvement was achieved and postsurgical orthodontic treatment was efficiently carried out. The total treatment time was 16 months. The patient's facial appearance improved significantly and a stable surgical orthodontic outcome was obtained. (Am J Orthod Dentofacial Orthop 2017;152:255-67) },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2021 |
Bianchi, J; Ruellas, A; Prieto, J C; Li, T; Soroushmehr, R; Najarian, K; Gryak, J; Deleat-Besson, R; Le, C; Yatabe, M; Gurgel, M; Turkestani, N A; Paniagua, B; Cevidanes, L: Decision support systems in temporomandibular Joint osteoarthritis: A review of data science and artificial intelligence applications.. In: Seminars in Orthodontics, vol. 27, no. 2, pp. 78-86, 2021. (Type: Journal Article | Abstract | Links | BibTeX | Tags: AAOF, Cone-beam computed tomography, Discrepency Index, malocclusion severity, mandibular asymmetry, orthodontic, Peer Assessment Rating Index, technique, vertical control, x-ray)@article{Bianchi2021, With the exponential growth of computational systems and increased patient data acquisition, dental research faces new challenges to manage a large quantity of information. For this reason, data science approaches are needed for the integrative diagnosis of multifactorial diseases, such as Temporomandibular joint (TMJ) Osteoarthritis (OA). The Data science spectrum includes data capture/acquisition, data processing with optimized web-based storage and management, data analytics involving in-depth statistical analysis, machine learning (ML) approaches, and data communication. Artificial intelligence (AI) plays a crucial role in this process. It consists of developing computational systems that can perform human intelligence tasks, such as disease diagnosis, using many features to help in the decision-making support. Patient's clinical parameters, imaging exams, and molecular data are used as the input in cross-validation tasks, and human annotation/diagnosis is also used as the gold standard to train computational learning models and automatic disease classifiers. This paper aims to review and describe AI and ML techniques to diagnose TMJ OA and data science approaches for imaging processing. We used a web-based system for multi-center data communication, algorithms integration, statistics deployment, and process the computational machine learning models. We successfully show AI and data-science applications using patients' data to improve the TMJ OA diagnosis decision-making towards personalized medicine. |
Turkestani, N Al; Bianchi, J; Deleat-Besson, R; et al,: Clinical decision support systems in orthodontics: A narrative review of data science approaches.. In: Orthod Craniofac Res, 2021. (Type: Journal Article | Abstract | Links | BibTeX | Tags: AAOF, clinical orthodontist, Cone-beam computed tomography, Cranial base, craniofacial, hyperdivergent, malocclusion severity, mandibular asymmetry, Posttreatment, technique)@article{Turkestani2021, 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. |
Boubolo, Louis; Dumont, Maxime; Brosset, Serge; Bianchi, Jonas; Ruellas, Antonio; Gurgel, Marcela; Massaro, Camila; Castillo, Aron Aliaga Del; Ioshida, Marcos; Yatabe, Marilia; Benavides, Erika; Rios, Hector; Soki, Fabiana; Neiva, Gisele; Paniagua, Beatriz; Cevidanes, Lucia; Styner, Martin; Prieto, Juan Carlos: FlyBy CNN: a 3D surface segmentation framework. In: Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115962B , 2021. (Type: Journal Article | Abstract | Links | BibTeX | Tags: AAOF, Cone-beam computed tomography, Cranial base, Growth, hyperdivergent, mandibular asymmetry, Mandibular fixed retainer, Peer Assessment Rating Index, Posttreatment, pressure tension, technique, vertical control)@article{Boubolo2021, 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. |
2020 |
Serge, B; Maxime, D; Bianchi, J; Antonio, R; Lucia, C; Marilia, Y; Joao, G; Erika, C; Fabiana, S; Beatriz, P; Juan, P; Kayvan, N; Jonathan, G; Reza, S: 3D Auto-Segmentation of Mandibular Condyles. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 1270-1273, 2020. (Type: Journal Article | Abstract | Links | BibTeX | Tags: AAOF, Adolescents, anterior openbite, Cone-beam computed tomography, Cranial base, craniofacial, Discrepency Index, extraction, hyperdivergent, mandibular asymmetry, Mandibular fixed retainer, Mandibular remodeling, orthodontic, Peer Assessment Rating Index, Posttreatment, teaching)@article{Brosset2020, 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. |
Bianchi, J; Goncalves, J R; de Oliveira Ruellas, A C; Ashman, L M; Vimort, J B; Yatabe, M; Paniagua, B; Hernandez, P; Benavides, E; Soki, F N; Loshida, M; Cevidanes, L H S: Quantitative bone imaging biomarkers to diagnose temporomandibular joint osteoarthritis. In: International Journal of Oral and Maxillofacial Surgery, vol. 50, no. 2, pp. 227-235, 2020. (Type: Journal Article | Abstract | Links | BibTeX | Tags: AAOF, Cone-beam computed tomography, Cranial base, Growth, hyperdivergent, mandibular asymmetry, Mandibular fixed retainer, Mandibular remodeling, orthodontic, pressure tension, technique)@article{Bianchi2020, 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. |
2019 |
Sam, A; Currie, K; Oh, H; Flores-Mir, C; Lagravere-Vich, M: Reliability of different 3D cephalometric landmarks in CBCT: A systematic review.. In: Angle Orthod, vol. 89, no. 2, pp. 317-332, 2019. (Type: Journal Article | Abstract | Links | BibTeX | Tags: AAOF, anterior openbite, Cone-beam computed tomography, Cranial base, hyperdivergent, mandibular asymmetry, Mandibular fixed retainer, Mandibular remodeling)@article{Sam2019, Conventional two-dimensional (2D) cephalometric radiography is an integral part of orthodontic patient diagnosis and treatment planning. One must be mindful of its limitations as it indeed is a 2D representation of a vaster three-dimensional (3D) object. Issues with projection errors, landmark identification, and measurement inaccuracies impose significant limitations, which may now be overcome with the advent of cone-beam computed tomography (CBCT). A systematic review of the reliability of different 3D cephalometric landmarks in CBCT imaging was conducted. |
Garnett, B; Mahod, K; Nguyen, M; Al-Khateeb, A; Liu, S; Boyd, R; Oh, H: Cephalometric comparison of adult anterior open bite treatment using clear aligners and fixed appliances.. In: Angle Orthodontist, vol. 89, no. 1, pp. 3-9, 2019. (Type: Journal Article | Abstract | Links | BibTeX | Tags: AAOF, Cone-beam computed tomography, Cranial base, craniofacial, Discrepency Index, extraction, fixed appliances, hyperdivergent, mandibular asymmetry, Mandibular fixed retainer, Mandibular remodeling, mapping, open bite, Peer Assessment Rating Index, Posttreatment, research, x-ray)@article{Garnett2019, To compare fixed appliances and clear aligner therapy in correcting anterior open bite and in controlling the vertical dimension in adult patients with hyperdivergent skeletal patterns. |
2018 |
Liu, S; Oh, H; Chambers, D; Baumrind, S; Xu, T: Interpreting Weightings of the Peer Assessment Rating Index and the Discrepancy Index across Contexts on Chinese Patients.. In: European Journal of Orthodontics, vol. 40, no. 2, pp. 157-163, 2018. (Type: Journal Article | Abstract | Links | BibTeX | Tags: clear aligners, clinical orthodontist, Cone-beam computed tomography, Cranial base, Growth, hyperdivergent, malocclusion severity, mandibular asymmetry, Peer Assessment Rating Index, teaching, vertical control)@article{Liu2017b, Determine optimal weightings of Peer Assessment Rating (PAR) index and Discrepancy Index (DI) for malocclusion severity assessment in Chinese orthodontic patients. |
2017 |
Currie, K; Sawchuk, D; Saltaji, H; Oh, H; Flores-Mir, C; Lagravere-Vich, M: Posterior cranial base natural growth and development: A systematic review. . In: Angle Orthodontist, vol. 87, no. 6, pp. 897-910, 2017. (Type: Journal Article | Abstract | Links | BibTeX | Tags: AAOF, adult, Cranial base, extraction, fixed appliances, Growth, mandibular asymmetry, Mandibular fixed retainer, Posttreatment, pressure tension, research, retrospective, vertical control, x-ray)@article{Currie2017b, To provide a synthesis of the published studies evaluating the natural growth and development of the human posterior cranial base (S-Ba). |
Hwang, Hyeon-Shik; Oh, Min-Hee; Oh, Hee-Kyun; Oh, Heesoo: Surgery-first approach in correcting skeletal Class III malocclusion with mandibular asymmetry. In: American Journal of Orthodontics and Dentofacial Orthopedics, vol. 152, no. 2, pp. 255-267, 2017. (Type: Journal Article | Abstract | Links | BibTeX | Tags: AAOF, adult, Class III, Cranial base, Discrepency Index, hyperdivergent, mandibular asymmetry, Mandibular fixed retainer, Mandibular remodeling, mapping, open bite, orthodontic, surgery-first)@article{Hwang2017, This case report describes a surgical orthodontic case that used the recently introduced surgery-first approach to correct a severe skeletal Class III malocclusion. A 19-year-old woman presented with severe mandibular prognathism and facial asymmetry; she had been waiting for growth completion in order to pursue surgical correction. After prediction of the postsurgical tooth movement and surgical simulation, 2-jaw surgery that included maxillary advancement and differential mandibular setback was performed using a surgery-first approach. Immediate facial improvement was achieved and postsurgical orthodontic treatment was efficiently carried out. The total treatment time was 16 months. The patient's facial appearance improved significantly and a stable surgical orthodontic outcome was obtained. (Am J Orthod Dentofacial Orthop 2017;152:255-67) |