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    深度学习模型有效地将多发性硬化症 (MS) 步态与健康个体区分开来,使用先进的行走模式分析. 这一框架显示了通过步态分析自动化MS诊断的前景.

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    科学领域:

    • 神经学 神经学
    • 生物医学工程 生物医学工程
    • 计算机科学 计算机科学

    背景情况:

    • 多发性硬化症 (MS) 是一种慢性中枢神经系统疾病,影响着运动能力.
    • 步行异常是MS的早期和频繁指标.
    • 精确区分MS步态对于诊断和管理至关重要.

    研究的目的:

    • 评估一个深度学习 (DL) 框架,DeepMS2G,用于对MS患者的步态进行分类 (PwMS) 与健康对照 (HC).
    • 评估框架在不同步行任务和看不见的参与者之间是否可通用.
    • 通过步态分析探索自动化MS诊断的潜力.

    主要方法:

    • 收集了20个HC和20个PwMS的步态数据,在单任务和双任务行走时使用仪器化跑步机.
    • 使用的领域基于知识的时空和运动步态特征与基于回归的规范化.
    • 对比了16个机器学习和DL算法,重点是剩余神经网络 (ResNet) 模型.

    主要成果:

    • 基于ResNet的模型与基于回归的规范化在分类MS步态方面取得了最高的表现.
    • 一个多尺度的ResNet在从单任务步行到双任务步行时实现了完美的精度 (1.0).
    • 在对未见的参与者进行概括时,ResNet模型实现了高精度 (0.83) 和F1得分 (0.81).

    结论:

    • 先进的DL和步态动态分析成功地在各种条件和参与者之间对MS步态进行了分类.
    • 深度MS2G框架在区分MS与健康步行方面表现出高度准确性.
    • 这些DL算法为自动化MS诊断提供了潜在的途径.