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深度学习系统用于在下肢计算机断层扫描上自动测量股骨旋转角度

Sheen-Woo Lee¹, Gi Pyo Lee², Ieun Yoon¹

¹Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 1021, Tongil-Ro, Eunpyeong-Gu, Seoul, Republic of Korea.

Journal of imaging informatics in medicine

|September 10, 2025

概括

一个新的深度学习算法可以自动测量CT扫描上的股骨和骨角. 这种人工智能工具准确地识别了地标并计算了旋转,大大减少了骨关节炎患者的分析时间.

科学领域:

医学成像医学成像
放射学中的人工智能
整形外科手术整形外科手术

背景情况:

精确测量股骨旋转对于诊断和管理下肢疾病,特别是骨关节炎至关重要.
从CT扫描中手动测量股骨和骨版本角度是耗时的,容易引起观察者之间的变化.

研究的目的:

开发和验证一个深度学习算法,用于自动识别解剖标志,并在下肢CT扫描上计算股骨和骨版本角度 (FTT角度).
与手动测量相比,评估自动化方法的准确性和效率.

主要方法:

一项回顾性研究分析了270名成年患者的CT扫描.
一个注意力U-Net模型被训练用于骨细分,地标检测和角度测量.
该算法的性能由肌肉骨放射科医生对手工测量进行了验证.

主要成果:

细分模型实现了高精度,92.16%的灵敏度,99.96%的特异性,以及93.12%的子相似系数.
自动化角度测量显示与放射科医生的测量有很好的相关性 (腿部为r=0.64,部为r=0.54).
自动化细分显著减少了每条腿的测量时间 (57.5s vs 79.6s).

结论:

深度学习方法可以自动测量CT扫描上的股骨旋转.
这种方法显示了在临床环境中加快患者数据分析的潜力.
自动化工具为骨科应用提供了可靠和高效的替代手工测量方法.

关键词:

自动测量自动测量自动测量计算机断层扫描 (CT) 是一种计算机断层扫描.深度学习是一种深度学习.雌乳旋转雌乳旋转模型模型模型模型模型

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