基于视频的指动力学用于退行性宫肌病:基于智能手机的计算机视觉方法用于查查
Viprav B Raju1, Ramesh M Arnest1, Amulya Setlur1
1Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, United States.
概括
一个新的智能手机工具使用手指运动分析准确检测退行性宫骨髓病 (DCM),超过了简单的握力计数. 这项技术有助于早期诊断这种脊髓疾病.
科学领域:
- 生物医学工程 生物医学工程
- 神经学 神经学
- 医疗成像医学成像
背景情况:
- 退行性宫髓病 (DCM) 是一种渐进的脊髓疾病,由于缺乏客观查工具,往往被诊断为晚期.
- 10秒握放试验是一种常见但有限的DCM手部功能障碍测量方法,特别是在患有并发症的老年人中.
- 目前DCM的诊断延迟在1至4年之间,这凸显了需要改进查方法的需要.
研究的目的:
- 开发和评估基于智能手机的计算机视觉工具,用于在握放测试期间量化手指动力学.
- 为了确定视频衍生指动力学是否为DCM提供了优越的诊断性能,而不是单纯的握力计数.
- 评估视频衍生动力学特征与通过MRI测量的椎脊髓压缩之间的相关性.
主要方法:
- 分析了58名DCM患者和65名对照人进行握放测试的智能手机视频.
- 使用MediaPipe提取指的地标,每指生成250个动态特征.
- 一个CatBoost分类器被训练在选定的特征上,一个后勤回归模型仅使用握力计数进行比较. 使用线性回归分析了MRI脊髓压缩的关联.
主要成果:
- 计算机视觉模型 (CatBoost) 使用动力学特征实现了高诊断性能 (AUC 0.90,F1-score 0.83),显著优于仅计握力模型 (AUC 0.69).
- 视频衍生动力学特征与宫脊髓压缩的MRI测量有显著的关联 (R2值高达0.45).
- 与对照组 (10.26) 相比,DCM组 (7.92) 的平均握力数量明显较低.
结论:
- 一个基于智能手机的计算机视觉工具有效地检测到DCM的手动运动损伤特征.
- 与单独的握力计数相比,手指动力学分析为DCM提供了更高的诊断准确性.
- 这项技术为DCM在各种环境中的早期,可扩展的查提供了一个有希望的途径.
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