在使用YOLO模型的全景放射图上基于深度学习检测开放顶牙的检测
Merve Edik1, Fatma Çelebi2, Aykağan Çukurluoğlu1
1Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Erciyes University, Melikgazi, Kayseri, Turkey.
Oral radiology
|December 23, 2025
概括
像YOLOv4这样的深度学习模型可以准确地从放射图中检测开放的顶部牙,从而有可能减少患者暴露于额外的X射线. 这种AI方法为牙科专业人员提供了更快,更精确的诊断.
科学领域:
- 牙科 牙科是指牙科的专业.
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 检测牙有开放的尖峰是治疗规划的关键.
- 传统方法可能需要多次放射,增加患者的辐射暴露.
- 深度学习为高效和准确的牙顶部检测提供了一个潜在的解决方案.
研究的目的:
- 评估和比较基于You Only Look Once (YOLO) 的深度学习模型的性能,以检测开放的顶部牙.
- 评估YOLOv3,YOLOv4和YOLOv5在全景放射图上识别开放顶牙的诊断准确度.
主要方法:
- 使用了966张全景放射图,开放顶部牙被标记成六个类别 (切口,前,牙).
- 训练和评估了YOLOv3,YOLOv4和YOLOv5的人工智能模型.
- 计算精度,回忆,平均平均精度 (mAP) 和F1得分来评估模型性能.
主要成果:
- YOLOv4显示了最高的整体性能,平均平均精度 (mAP@0.5) 为87.84%.
- YOLOv4获得了最高的回忆率 (90%) 和F1得分 (0.87).
- 所有评估的YOLO模型 (v3,v4,v5) 在关键性能指标中都超过了84%.
结论:
- 基于YOLO的深度学习模型显示,对于检测开放的顶部牙显著有前途.
- 这些人工智能系统可以提供更快,更准确的诊断,可能减少对额外放射图的需求.
- 这些发现表明,深度学习可以通过提高诊断效率和患者护理来帮助牙医.
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