在使用YOLO模型的不同放射视图中构建部高原骨折检测
Shun-Ping Wang1,2, Han-Ting Shih2,3, Yu-Xiang Liao4
1Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung City 40227, Taiwan.
Diagnostics (Basel, Switzerland)
|January 28, 2026
概括
使用前后 (AP) X射线视图的深度学习模型显著改善了骨平原骨折检测. 在AP图像上训练的YOLOv9模型实现了最高的诊断准确度,证明了AI.
科学领域:
- 放射学 放射学是指放射学
- 人工智能的人工智能
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 由于3D可视化有限,骨高原骨折在常规X射线成像中存在诊断挑战.
- 深度学习,特别是你只看一次 (YOLO) 模型,为增强骨折检测提供了潜力.
研究的目的:
- 评估四个YOLO深度学习模型 (YOLOv4,YOLOv5,YOLOv8,YOLOv9) 在检测骨平原骨折方面的性能.
- 为了比较不同放射视图 (前后,侧面,组合) 训练的模型的疗效.
主要方法:
- 使用了1489张膝盖X射线 (727张骨折,762张无骨折) 的数据集.
- 训练了YOLOv4,YOLOv5,YOLOv8和YOLOv9模型,使用前后 (AP),横向和综合放射视图.
- 模型性能使用包括精度,灵敏度,特异性,精度,F1得分和曲线下面积 (AUC) 在内的指标进行评估.
主要成果:
- 在AP视图上训练的模型始终优于那些在横向或组合视图上训练的模型.
- 在AP图像上训练的YOLOv9在所有指标 (准确性,特异性,精度,F1得分,AUC) 上实现了最高的性能,灵敏度和NPV的值高达0.99和1.00.
- 外部验证证明了强大的通用性,AP训练的YOLOv9获得了0.87的精度和0.93.9的AUC.
结论:
- 在AP放射视图上训练深度学习模型显著提高了骨高原骨折的诊断准确性.
- 特别是在AP视图上训练时,YOLOv9表现出卓越的性能,突出了先进的深度学习架构的优势.
- 人工智能辅助的YOLO模型在改善骨高原骨折的检测方面显示出相当大的希望.
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