ID | 118606 |
Title Alternative | Development of an AI-based Dental Support System for Panoramic X-ray Images
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Author |
Torii, Kohei
Tokushima University
Honda, Eiichi
Tokushima University
Tokushima University Educator and Researcher Directory
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Kita, Kenji
Tokushima University
Tokushima University Educator and Researcher Directory
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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
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Content Type |
Journal Article
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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.
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Journal Title |
歯科放射線
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ISSN | 03899705
21856311
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NCID | AN00101479
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Publisher | 日本歯科放射線学会
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Volume | 62
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Issue | 1
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Start Page | 24
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End Page | 34
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Published Date | 2022
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EDB ID | |
DOI (Published Version) | |
URL ( Publisher's Version ) | |
FullText File | |
language |
jpn
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TextVersion |
Publisher
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departments |
Oral Sciences
Science and Technology
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