Artificial intelligent recognition for multiple supernumerary teeth in periapical radiographs based on faster R-CNN and YOLOv8 - 23/02/25

Doi : 10.1016/j.jormas.2025.102293

Jiajia Zheng ^a, Hong Li ^a,^⁎ , Quan Wen ^a, Yuan Fu ^a, Jiaqi Wu ^a, Hu Chen ^b
^a Doctor and Researcher, First Clinical Division, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, 37A Xishiku Street, Xicheng District, Beijing, 100034, PR China
^b Associate Clinical Professor, Center for Digital Dentistry, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology, No.22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, PR China

^⁎Corresponding author at: First Clinical Division, Peking University School and Hospital of Stomatology, 37A Xishiku Street, Xicheng District, Beijing, 100034, PR China.First Clinical DivisionPeking University School and Hospital of Stomatology37A Xishiku StreetXicheng DistrictBeijing100034PR China

Sous presse. Épreuves corrigées par l'auteur. Disponible en ligne depuis le Sunday 23 February 2025

Abstract

Objectives

The aim of this study was to compare the effectiveness of automated supernumerary tooth (ST) detection systems on periapical radiographs using Faster R-CNN and YOLOv8 with detection by 8 dental residents.

Methods

This was a diagnostic accuracy study of 469 periapical radiographs (419 training vs. 50 test datasets). The primary predictor variables were detectors (dental residents/Faster R-CNN/YOLOv8). The main outcome variables included the diagnostic performance of the model's using precision, recall and intersection over union (IoU). Appropriate statistics were calculated.

Results

In the test dataset, the precision of Faster R-CNN and YOLOv8 was 0.95 and 0.99, and their average precision was 0.90 and 0.97, respectively. A significant difference was observed between the two models in these metrics, with YOLOv8 outperforming Faster R-CNN in both precision and average precision (P<0.05). Both AI systems outperformed human subjects.

Conclusions

Based on our findings, both YOLOv8 and Faster R-CNN are highly effective in the automated detection of ST in periapical radiographs and could, for example, assist humans in resource-limited situations.

Le texte complet de cet article est disponible en PDF.

Keywords : Supernumerary teeth, Faster region-based convolutional neural network (Faster R-CNN), YOLOv8, Periapical radiographs