Artificial intelligent recognition for multiple supernumerary teeth in periapical radiographs based on faster R-CNN and YOLOv8.

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.