Abstract:

Stroke is a common disease caused by cerebrovascular disorders. Its high-risk factors include hypertension, diabetes mellitus, aging, etc. Post-stroke hand dysfunction, one of the common sequelae, manifests as reduced muscle strength, impaired coordination, and loss of fine motor skills, and directly affects patients' daily living activities (e.g., grasping, eating, dressing, and so on), occupational development, and learning abilities, while potentially triggering psychological issues such as depression and anxiety. Consequently, disease assessment is critical in its subsequent treatment. In recent years, artificial intelligence technologies have provided new pathways for dynamic monitoring and evaluation alongside traditional methods. This article reviews and analyzes clinically used assessment approaches and emerging technologies for evaluating post-stroke hand dysfunction.

Key words:

Stroke, Hand dysfunction, Rehabilitation assessment, Progress

摘要：

脑卒中是由脑血管病变引起的一种常见疾病，高危因素包括高血压、糖尿病、老龄化等。手功能障碍作为脑卒中常见后遗症之一，表现为肌力下降、协调性缺失和精细动作能力丧失，直接影响患者的日常生活（如抓握、进食、穿衣等）、职业发展和学习能力，并可能引发抑郁、焦虑等心理问题。因此，对疾病进行评估在后续治疗过程中尤为重要。近年来，除传统评估方法外，人工智能技术为动态监测和评估提供了新路径。本文主要对临床用于评估脑卒中后手功能障碍的常用方法和新兴技术进行综述与分析。

关键词:

脑卒中, 手功能障碍, 康复评估, 进展