Author:
Ragodos Ronilo,Wang Tong,Padilla Carmencita,Hecht Jacqueline T.,Poletta Fernando A.,Orioli Iêda M.,Buxó Carmen J.,Butali Azeez,Valencia-Ramirez Consuelo,Restrepo Muñeton Claudia,Wehby George L.,Weinberg Seth M.,Marazita Mary L.,Moreno Uribe Lina M.,Howe Brian J.
Abstract
AbstractChildren with orofacial clefting (OFC) present with a wide range of dental anomalies. Identifying these anomalies is vital to understand their etiology and to discern the complex phenotypic spectrum of OFC. Such anomalies are currently identified using intra-oral exams by dentists, a costly and time-consuming process. We claim that automating the process of anomaly detection using deep neural networks (DNNs) could increase efficiency and provide reliable anomaly detection while potentially increasing the speed of research discovery. This study characterizes the use of` DNNs to identify dental anomalies by training a DNN model using intraoral photographs from the largest international cohort to date of children with nonsyndromic OFC and controls (OFC1). In this project, the intraoral images were submitted to a Convolutional Neural Network model to perform multi-label multi-class classification of 10 dental anomalies. The network predicts whether an individual exhibits any of the 10 anomalies and can do so significantly faster than a human rater can. For all but three anomalies, F1 scores suggest that our model performs competitively at anomaly detection when compared to a dentist with 8 years of clinical experience. In addition, we use saliency maps to provide a post-hoc interpretation for our model’s predictions. This enables dentists to examine and verify our model’s predictions.
Funder
National Institutes of Health
Robert Wood Johnson Foundation
Publisher
Springer Science and Business Media LLC
Reference26 articles.
1. Eerens, K. et al. Hypodontia and tooth formation in groups of children with cleft, siblings without cleft, and nonrelated controls. Cleft Palate Craniofac. J. 38, 374–378 (2001).
2. Letra, A., Menezes, R., Granjeiro, J. & Vieira, A. Defining subphenotypes for oral clefts based on dental development. J. Dent. Res. 86, 986–991 (2007).
3. Rawashdeh, M. A. & Sirdaneh, E. O. A. Crown morphologic abnormalities in the permanent. J. Craniofac. Surg. 20, 465–470 (2009).
4. Walker, S. C., Mattick, C. R., Hobson, R. S. & Steen, I. N. Abnormal tooth size and morphology in subjects with cleft lip and/or palate in the north of England. Eur. J. Orthod. 31(1), 68–75. https://doi.org/10.1093/ejo/cjn073 (2009).
5. Wu, T.-T., Chen, P. K. T., Lo, L.-J., Cheng, M.-C. & Ko, E.W.-C. The characteristics and distribution of dental anomalies in patients with cleft. Chang Gung Med. J. 34(3), 306–314 (2011).
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