可解释的人工智能安全平衡及其在中风患者的主要决定因素
Sekwang Lee1, Eunyoung Lee2, Kwang-Sig Lee3
1Department of Physical Medicine and Rehabilitation, Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
Scientific reports
|October 10, 2024
概括
使用可解释的AI,可以预测中风后的安全平衡. 早期运动功能和扩散张力成像可以预测移动性结果,帮助康复策略.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 康复医学 康复医学 康复医学
背景情况:
- 脑卒中严重损害了运动和平衡,需要准确预测康复.
- 预测安全平衡对于患者的康复和中风后独立至关重要.
- 目前的预测方法可能无法充分利用多式联络患者数据.
研究的目的:
- 开发和验证一种可解释的人工智能 (AI) 模型,用于预测中风幸存者的安全平衡.
- 确定安全平衡的关键预测因素,包括临床,神经生理学和扩散张力成像 (DTI) 数据.
- 将人工智能模型的性能与传统的物流回归进行比较.
主要方法:
- 追溯分析92名首次中风患者的数据.
- 利用随机森林模型与沙普利添加式解释 (SHAP) 进行预测和解释性.
- 包括Berg Balance Scale (BBS) 评分,Fugl-Meyer Assessment (FMA) 评分,肌肉强度和DTI属性 (微分异型) 作为预测因素.
主要成果:
- 随机森林模型实现了高精度 (AUC 91-92%),超过了后勤回归 (AUC 87-92%).
- 3个月后BBS的关键预测因素包括1个月后的BBS,FMA和ipsilesional皮质脊髓系的分数异构.
- 6个月后BBS的关键预测因素包括FMA,1个月后BBS和下肢肌肉力量.
结论:
- 在中风后恢复安全平衡与初始运动功能和皮质脊柱管完整性密切相关.
- 可以解释的AI,集成DTI数据,增强了中风后安全平衡的预测.
- 开发的AI模型提供了对影响平衡恢复的因素的见解,有助于个性化康复.
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