基于机器学习的脑卒中上肢运动功能智能评定系统的可信度和准确性研究

关键词：脑卒中  机器学习  Brunnstrom运动功能分期  康复评定

Keywords:stroke  machine learning  Brunnstrom motor function staging  rehabilitation assessment

摘要：

      摘要 目的：为了实现脑卒中患者上肢运动功能障碍的远程居家智能评定和功能训练指导，本文探讨基于机器学习的脑卒中上肢运动功能智能评定系统评定脑卒中患者上肢和手部Brunnstrom运动功能分期的可信度和准确性。 方法：选取2024年8月1日—2025年6月30日在中山大学附属第一医院康复医学科收治的脑卒中患者，共纳入符合条件的受试者220例。其中，160例脑卒中患者的数据被用于训练脑卒中上肢运动功能智能评定系统的模型，在视频指导下完成指定动作并由专业治疗师拍摄视频，通过治疗师人工评定和智能评定系统评定，并且利用机器学习模型优化智能评定系统。余下的60例患者对比人工和智能系统评定结果，分析基于机器学习的脑卒中上肢运动功能智能评定系统的可信度和准确性。 结果：基于机器学习的脑卒中上肢运动功能智能评定系统的重测信度较高（上肢ICC为0.989，手ICC为0.969）。不同评定者使用智能评定系统评定的测量结果的一致性较高（上肢ICC为0.896；手ICC为0.959）。此智能评定系统 的上肢分期评定Kappa值为0.900（P＜0.001），手分期评定Kappa值为0.816（P＜0.001）。 结论：基于机器学习的脑卒中上肢运动功能智能评定系统具有较好的可信度和准确性，可作为脑卒中患者上肢运动功能远程康复的评定工具。

Abstract:

      Abstract Objective： To facilitate remote home-based intelligent assessment and rehabilitation guidance for upper limb motor dysfunction in stroke patients， this study aimed to explore the reliability and accuracy of a machine-learning-based intelligent assessment system for evaluating Brunnstrom stages of upper limb and hand motor function in stroke patients. Method： A total of 220 patients with stroke who were admitted to the Department of Rehabilitation Medicine of the First Affiliated Hospital of Sun Yat-sen University from August 1， 2024 to June 30， 2025 were enrolled. Data from 160 stroke patients were used to train and optimize the machine-learning model of the intelligent assessment system for upper-limb motor function in stroke patients. Patients completed standardized movements under video guidance and were recorded by trained therapists. Brunnstrom stages of upper limb and hand motor function were independently evaluated by therapists and the intelligent assessment system. The remaining 60 stroke patients were used to validate the system by comparing automated assessments with therapist ratings. Result： The machine learning-based intelligent assessment system demonstrated excellent test-retest reliability, with intraclass correlation coefficients (ICCs) of 0.989 for upper limb staging and 0.969 for hand staging. The inter-rater reliability for the intelligent assessment system was also high （upper limb ICC was 0.896； hand ICC was 0.959）. Agreement between the intelligent assessment system and therapist ratings was excellent, with a Kappa coefficient of 0.900 （P＜0.001） for upper limb staging and 0.816 （P＜0.001） for hand staging. Conclusion： The machine-learning-based intelligent assessment system demonstrated good reliability and accuracy in assessing upper limb motor function after stroke and may serve as a practical assessment tool for remote upper limb function evaluation and monitoring in patients with stroke.