School of Medicine and Surgery, Benito Juárez Autonomous University of Oaxaca, Mexico.
CONACyT- School of Dentistry, Benito Juárez Autonomous University of Oaxaca, Mexico.
Adv Clin Exp Med. 2022 Dec;31(12):1309-1318. doi: 10.17219/acem/152596.
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.
在脑卒中后的康复方案中，运动功能的评估至关重要，因此必须对运动功能进行客观、定量的测量。一些创新的机器学习算法可用于实现对上肢运动功能的自动评估。
对用于评估脑卒中后患者上肢运动功能的机器学习算法的疗效进行系统评价和荟萃分析，并将这些算法与临床评估进行比较。
本研究方案已在国际前瞻性系统评价登记处（PROSPERO）数据库中注册。该综述按照系统评价和荟萃分析的首选报告项目（PRISMA）指南以及 Cochrane 干预系统评价手册进行。通过 6 个电子数据库进行检索。使用随机模型对相关系数的数据进行荟萃分析。
最初的搜索共产生了 1626 条记录，但只有 8 项研究完全符合纳入标准。这些研究报告了所测试的算法与临床评估之间存在很强和非常强的相关性。荟萃分析显示存在异质性（I2 = 85.29%，Q = 48.15），这归因于纳入研究的异质性。
使用机器学习算法的自动化系统可以帮助治疗师评估脑卒中后患者的上肢运动功能。然而，为了得出更可靠的结论，需要采用能够降低偏倚风险并提高未来研究方法学质量的方法学设计。
