评估YOLO用于牙 кариес诊断:一个系统的审查和元分析
Quang Tuan Lam1,2,3, Minh Huu Nhat Le2,4, I-Ta Lee5
1School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan.
Evidence-based dentistry
|July 23, 2025
概括
你只看一次 (YOLO) 算法有效地检测牙虫. 使用YOLO的放射图像显示出更高的特异性,而摄影方法提供无辐射查,使YOLO成为诊断牙损伤的可靠工具.
科学领域:
- 人工智能在牙科中的应用
- 机器学习用于医学成像
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- "你只看一次" (YOLO) 算法的最新进展显示,牙损伤的诊断具有前途.
- 评估不同YOLO版本的诊断性能对于临床应用至关重要.
研究的目的:
- 为了评估各种You Only Look Once (YOLO) 算法版本的诊断性能,用于检测牙损伤.
- 为了比较YOLO的有效性,使用摄影与X射线牙科图像.
主要方法:
- 使用PubMed,EMBASE,科学网络和Scopus,对研究进行系统审查和元分析,截至2024年12月12日.
- 包括使用任何YOLO版本检测牙损伤的研究.
- 使用双变随机效应模型计算聚合的灵敏度,特异性和曲线下的面积 (AUC).
主要成果:
- 综述中包括15项研究;元分析中包括14项研究.
- 整体聚合性能:灵敏度为79.3%,特异性为84.9%,AUC为0.832.
- 与摄影模型相比,放射性YOLO模型的特异性 (92.5%) 和AUC (0.847) 较高,分别为72.0%和0.735.
结论:
- YOLO算法可靠地检测牙腐烂.
- 基于X射线图的YOLO模型提供了卓越的诊断特异性.
- 摄影YOLO方法是无辐射的,适合用于低资源查;旧的YOLO版本仍然有效.
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