荟萃分析
系统回顾
机器学习
算法
偏瘫
康复
协议(科学)
物理医学与康复
梅德林
人工智能
冲程(发动机)
计算机科学
医学
物理疗法
外科
替代医学
政治学
内科学
法学
病变
机械工程
工程类
病理
作者
Jorge Fernando Ambros-Antemate,Adriana Reyes-Flores,Liliana Argueta-Figueroa,Ramírez-Ramírez R,Marciano Vargas Treviño,Jaime Gutiérrez-Gutiérrez,Eduardo Pérez-Campos,Eduardo Pérez-Campos,Luis Angel Flores-Mejía,Rafael Torres-Rosas
出处
期刊:Advances in Clinical and Experimental Medicine
[Wroclaw Medical University]
日期:2022-09-01
卷期号:31 (12)
被引量:1
摘要
The assessment of motor function is vital in post-stroke rehabilitation protocols, and it is imperative to obtain an objective and quantitative measurement of motor function. There are some innovative machine learning algorithms that can be applied in order to automate the assessment of upper extremity motor function.To perform a systematic review and meta-analysis of the efficacy of machine learning algorithms for assessing upper limb motor function in post-stroke patients and compare these algorithms to clinical assessment.The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database. The review was carried out according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Cochrane Handbook for Systematic Reviews of Interventions. The search was performed using 6 electronic databases. The meta-analysis was performed with the data from the correlation coefficients using a random model.The initial search yielded 1626 records, but only 8 studies fully met the eligibility criteria. The studies reported strong and very strong correlations between the algorithms tested and clinical assessment. The meta-analysis revealed a lack of homogeneity (I2 = 85.29%, Q = 48.15), which is attributable to the heterogeneity of the included studies.Automated systems using machine learning algorithms could support therapists in assessing upper extremity motor function in post-stroke patients. However, to draw more robust conclusions, methodological designs that minimize the risk of bias and increase the quality of the methodology of future studies are required.
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