Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs

Author:

Kılıc Münevver Coruh1,Bayrakdar Ibrahim Sevki2,Çelik Özer3,Bilgir Elif2,Orhan Kaan4,Aydın Ozan Barıs1,Kaplan Fatma Akkoca2,Sağlam Hande2,Odabaş Alper3,Aslan Ahmet Faruk3,Yılmaz Ahmet Berhan5

Affiliation:

1. Department of Paediatric Dentistry, Faculty of Dentistry, Ataturk University, Erzurum, Turkey

2. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey

3. Department of Mathematics-Computer, Eskisehir Osmangazi University Faculty of Science, Eskisehir, Turkey

4. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey

5. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ataturk University, Erzurum, Turkey, Turkey

Abstract

Objective: This study evaluated the use of a deep-learning approach for automated detection and numbering of deciduous teeth in children as depicted on panoramic radiographs. Methods and materials: An artificial intelligence (AI) algorithm (CranioCatch, Eskisehir-Turkey) using Faster R-CNN Inception v2 (COCO) models were developed to automatically detect and number deciduous teeth as seen on pediatric panoramic radiographs. The algorithm was trained and tested on a total of 421 panoramic images. System performance was assessed using a confusion matrix. Results: The AI system was successful in detecting and numbering the deciduous teeth of children as depicted on panoramic radiographs. The sensitivity and precision rates were high. The estimated sensitivity, precision, and F1 score were 0.9804, 0.9571, and 0.9686, respectively. Conclusion: Deep-learning-based AI models are a promising tool for the automated charting of panoramic dental radiographs from children. In addition to serving as a time-saving measure and an aid to clinicians, AI plays a valuable role in forensic identification.

Publisher

British Institute of Radiology

Subject

General Dentistry,Radiology Nuclear Medicine and imaging,General Medicine,Otorhinolaryngology

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