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ID 118606
Title Alternative
Development of an AI-based Dental Support System for Panoramic X-ray Images
Author
Torii, Kohei Tokushima University
Keywords
パノラマX線画像
人工知能
画像認識
歯科支援システム
panoramic X-ray images
artificial intelligence(AI)
image classification
dental support system
Content Type
Journal Article
Description
Research and development of AI-based diagnostic systems in the medical and dental fields is flourishing worldwide. However, there are few practical dental support systems and dental databases. Support for medical record input is needed to reduce the burden at diagnosis. We have been developing a dental database with detailed annotation information and building an AI system for automatic teeth detection, teeth numbering, teeth contour estimation, and disease diagnosis from panoramic X-ray images since 2019. Three dentists and one expert of dental radiology created our database, which includes teeth number based on FDI method, coordinates of teeth contour, and various dental conditions, using Anotee, a software developed for creating dental databases. Our system consists of multiple deep neural networks that were trained using 1,781 panoramic X-ray images and annotations, which excluded deciduous teeth and rare dental conditions. The deep neural network to classify dental conditions is based on EfficientNetV2-S and can diagnose multiple dental conditions. To verify our system’s usefulness, we evaluated two dental condition classifications for 20 dental conditions and 10 dental conditions such as caries, periodontitis, root canal filling, inlay, composite resin, crown, pontic, implant, and impacted tooth. We performed 5-fold cross validation and calculated precision, sensitivity, and specificity. Experimental results were encouraging. For the diagnosis of 20 conditions, precision was 90.4%, sensitivity was 86.1%, and specificity was 99.4%; for the diagnosis of 10 conditions, precision was 92.9%, sensitivity was 90.0%, and specificity was 99.1%. The system achieved high accuracy, suggesting that AI systems are useful in assisting medical record input support.
Journal Title
歯科放射線
ISSN
03899705
21856311
NCID
AN00101479
Publisher
日本歯科放射線学会
Volume
62
Issue
1
Start Page
24
End Page
34
Published Date
2022
EDB ID
DOI (Published Version)
URL ( Publisher's Version )
FullText File
language
jpn
TextVersion
Publisher
departments
Oral Sciences
Science and Technology