发表机构
Lake Lucerne Institute; ETH Zurich; Shirley Ryan AbilityLab; Northwestern University（卢塞恩湖研究所; 苏黎世联邦理工学院; 雪莉·瑞安能力实验室; 西北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究评估临床常规ARAT评估中嵌入AI - 无标记运动捕捉能否准确重建上肢运动及产生有效运动学指标。通过对患者测试发现其生物力学重建准确稳健，运动学指标有特异性和敏感性，能补充序数评分。
AI 中文摘要
动作研究臂测试（ARAT）是神经康复中广泛使用的上肢结果测量方法，但其序数评分主观，敏感性和特异性有限。我们评估了在临床常规的ARAT评估中嵌入基于人工智能（AI）的无标记运动捕捉（MMC），是否能准确重建上肢运动并产生有效的、客观的运动学指标，这些指标携带超越序数评分的临床有意义信息。在对20名混合神经疾病患者的47次 sessions（1174项ARAT任务）中，生物力学重建在不同损伤水平上准确且稳健，运动学指标显示出符合结构效度测量的区分模式。在纵向案例研究中，这些指标增加了序数评分所缺乏的特异性和敏感性：域分解揭示了同等ARAT增益下患者特定的恢复概况（特异性），并且在ARAT饱和后仍能检测到运动学改善（敏感性）。因此，临床常规中的MMC可以提供有效的、客观的、敏感的和特异的运动学测量，补充序数评分。
英文摘要
The Action Research Arm Test (ARAT) is a widely-used upper limb outcome measure in neurorehabilitation, but its ordinal scoring is subjective and suffers from limited sensitivity and specificity. We evaluated whether artificial-intelligence (AI)-based markerless motion capture (MMC), embedded into ARAT assessments during clinical routine, accurately reconstructs upper limb movement and yields valid, objective kinematic metrics carrying clinically meaningful information beyond the ordinal score. Across 47 sessions from 20 mixed-neurological patients (1,174 ARAT tasks), biomechanical reconstruction was accurate and robust across impairment levels, and kinematic metrics showed the discrimination pattern expected of a construct-valid measure. In longitudinal case studies, the metrics added the specificity and sensitivity the ordinal score lacks: a domain decomposition exposed patient-specific recovery profiles underlying equal ARAT gains (specificity), and kinematic improvement continued to be detected after the ARAT had saturated (sensitivity). MMC in clinical routine can thus provide valid, objective, sensitive, and specific kinematic measurement complementing ordinal scoring.
