牙到白斑病变YOLO:一种新的白斑病变检测模型
Hau Man Chung1, Jingjing Ke1, Mengdan Zhang1
1Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Stomatology, No. 56 Lingyuan West Road, Guangzhou, Guangdong, 510055, China.
BMC oral health
|October 10, 2025
概括
一个新的深度学习模型,牙到WSL你只看一次 (TW-YOLO),显著改善了在正牙患者中检测白斑病变 (WSLs). 这种人工智能工具比原始YOLOv5模型更准确地识别这些常见的牙科问题.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 牙科成像 牙科成像 牙科成像
背景情况:
- 白斑病变 (WSL) 在接受正牙治疗的患者中很普遍.
- 准确检测WSL对于有效的患者管理至关重要.
- 现有的检测方法可能在准确性和效率方面存在局限性.
研究的目的:
- 开发和评估一种新的深度学习模型,TW-YOLO,用于检测WSLs.
- 为了比较TW-YOLO与原始YOLOv5模型的性能.
- 评估拟议的WSL检测模型的准确性和可解释性.
主要方法:
- 一组653张WSL口内摄影的数据集被策划和注释.
- TW-YOLO和YOLOv5模型使用457张图像进行了微调,在130张图像上进行了验证,并在66张图像上进行了测试.
- 使用科恩的kappa,mAP@0.5,mAP@0.5:0.95和F1得分来评估表现,并使用分数-CAM来解释.
主要成果:
- TW-YOLO获得了0.76的科恩卡帕,超过了YOLOv5的0.62.
- 与YOLOv5 (mAP@0.5:0.69,mAP@0.5:0.95:0.45) 相比,TW-YOLO的平均精度得分更高 (mAP@0.5:0.78,mAP@0.5:0.95:0.51).与YOLOv5 (mAP@0.5:0.69,mAP@0.5:0.95:0.45) 相比,TW-YOLO的平均精度得分更高.
- 可解释性分析表明,TW-YOLO将注意力集中在WSL地区,学习它们的分布模式.
结论:
- 与原来的YOLOv5.5相比,新型TW-YOLO模型在检测白斑病变方面表现出卓越的准确性.
- 改进归因于处理较小的物体,并以原始分辨率分析牙面膜.
- TW-YOLO显示出在矯正牙科中增强临床应用的前景.
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