在相同的EDSS等级内,人工智能协助识别残疾模式
Martina Greselin1,2,3, Po-Jui Lu1,2,3, Magdalena Mroczek2,3
1Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland.
Multiple sclerosis (Houndmills, Basingstoke, England)
|April 18, 2025
概括
扩展残疾状况尺度 (EDSS) 可能无法完全捕捉多发性硬化症 (MS) 的残疾. 人工智能分析显示,在相同的EDSS分数内,有不同的残疾模式,提高了评估准确性.
科学领域:
- 神经学 神经学
- 生物统计学 生物统计学
- 人工智能在医学中的应用
背景情况:
- 扩展残疾状况尺度 (EDSS) 是多发性硬化症 (MS) 试验的标准.
- EDSS分数≥4.5主要评估行走,可能缺少其他功能障碍.
研究的目的:
- 调查是否相同的EDSS分数可以代表不同的残疾模式.
- 完善逐步发展的MS中残疾评估.
主要方法:
- 从二次进展性多发性硬化症患者的EXPAND试验中分析了13,103项评估.
- 使用的功能系统得分,子得分和行走数据.
- 应用机器学习集群用于在EDSS类别中识别不同的子分值模式.
主要成果:
- 确定了许多代表类似残疾模式的集群.
- 在相同的EDSS得分中,对EDSS ≥4.0.0的患者进行了区分,区分了四种不同的分值模式.
结论:
- 人工智能可以在具有相同EDSS分数的患者中识别各种残疾概况.
- 这种方法提高了MS临床试验中残疾评估的细节性.
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