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Published on: February 23, 2024
Artificial intelligent recognition for multiple supernumerary teeth in periapical radiographs based on faster R-CNN
Jiajia Zheng1, Hong Li1, Quan Wen1
1Doctor 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.
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.
