Zheng Jiajia, Li Hong, Wen Quan, Fu Yuan, Wu Jiaqi, Chen Hu
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.
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.
J Stomatol Oral Maxillofac Surg. 2025 Feb 19:102293. doi: 10.1016/j.jormas.2025.102293.
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.
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.
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.
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.
本研究旨在比较使用Faster R-CNN和YOLOv8的根尖片自动多生牙（ST）检测系统与8名牙科住院医师检测的有效性。
这是一项对469张根尖片（419个训练数据集与50个测试数据集）的诊断准确性研究。主要预测变量是检测器（牙科住院医师/Faster R-CNN/YOLOv8）。主要结果变量包括模型使用精度、召回率和交并比（IoU）的诊断性能。计算了适当的统计数据。
在测试数据集中，Faster R-CNN和YOLOv8的精度分别为0.95和0.99，它们的平均精度分别为0.90和0.97。在这些指标上，两个模型之间观察到显著差异，YOLOv8在精度和平均精度方面均优于Faster R-CNN（P<0.05）。两个人工智能系统的表现均优于人类受试者。
基于我们的研究结果，YOLOv8和Faster R-CNN在根尖片ST的自动检测中都非常有效，例如在资源有限的情况下可以协助人类。
