使用深度学习进行远距离半径骨折的自动细分和放射测量
Sanglim Lee1, Kwang Gi Kim2, Young Jae Kim2
1Department of Orthopedic Surgery, Inje University Sanggye Paik Hospital, Seoul, Korea.
Clinics in orthopedic surgery
|February 2, 2024
概括
这项研究介绍了一种深度学习算法,用于测量X射线上的远半径骨折参数. 人工智能准确地测量了辐射倾斜,倾斜和高度,与外科医生的测量有很高的相关性.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 整形外科手术 整形外科手术
背景情况:
- 深度学习越来越多地应用于医学成像分析.
- 远半径骨折很常见,需要精确的放射学评估.
研究的目的:
- 开发和验证深度学习算法,用于测量远半径骨折的关键放射学参数.
- 为了比较自动测量与由手术手术医生进行的测量.
主要方法:
- 使用注意力U-Net和RetinaNet的算法被开发用于半径和骨细分.
- 该算法在634张手腕X射线 (AP和横视图) 上进行了训练和验证.
- 放射性参数 (辐射倾斜,倾斜,高度) 被自动测量并与外科医生测量结果进行比较.
主要成果:
- 在AP图像上进行半径细分的高精度 (99.98%) 和子相似系数 (98.07%) .
- 卓越的相关系数 (皮尔森的r>0.94,ICC>0.96) 辐射倾斜和倾斜.
- 对于辐射高度有很好的相关性 (皮尔森的r=0.768,ICC=0.868).
结论:
- 深度学习算法在X射线中准确地对距离半径和肘部进行细分.
- 该算法可提供对远半径骨折的关键放射学参数的可靠自动测量.
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