Journal article
Dental anomaly detection using intraoral photos via deep learning
Scientific reports, Vol.12(1), pp.1-8
07/01/2022
DOI: 10.1038/s41598-022-15788-1
PMCID: PMC9270352
PMID: 35804050
Abstract
Abstract Children 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.
Details
- Title: Subtitle
- Dental anomaly detection using intraoral photos via deep learning
- Creators
- Ronilo Ragodos - University of IowaTong Wang - University of IowaCarmencita Padilla - University of the Philippines ManilaJacqueline T. Hecht - The University of Texas Health Science Center at HoustonFernando A. Poletta - ECLAMC at Center for Medical Education and Clinical Research, CEMIC-CONICET, Buenos Aires, Argentina.Iêda M. Orioli - Universidade Federal do Rio de JaneiroCarmen J. Buxó - University of Puerto Rico SystemAzeez Butali - University of IowaConsuelo Valencia-Ramirez - Clinica Noel, Medellín, Colombia.Claudia Restrepo Muñeton - Clinica Noel, Medellín, Colombia.George L. Wehby - University of IowaSeth M. Weinberg - University of PittsburghMary L. Marazita - University of PittsburghLina M. Moreno Uribe - University of IowaBrian J. Howe - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Scientific reports, Vol.12(1), pp.1-8
- DOI
- 10.1038/s41598-022-15788-1
- PMID
- 35804050
- PMCID
- PMC9270352
- NLM abbreviation
- Sci Rep
- eISSN
- 2045-2322
- Publisher
- Nature Portfolio
- Grant note
- DOI: 10.13039/100000002, name: National Institutes of Health, award: R00 DE022378, R01 DD000295, R01 DE016148, K08 DE028012; DOI: 10.13039/100000867, name: Robert Wood Johnson Foundation, award: 72429
- Language
- English
- Date published
- 07/01/2022
- Academic Unit
- Preventive and Community Dentistry; Orthodontics; Oral Pathology, Radiology and Medicine; Health Management and Policy; Stead Family Department of Pediatrics; Economics; Dental Clinic Administration; Craniofacial Anomalies Research Center; Public Policy Center (Archive); Business Analytics; Family Dentistry; Dental Research
- Record Identifier
- 9984273660102771
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