Accuracy of machine learning algorithms for the assessment of upper-limb motor impairments in patients with post-stroke hemiparesis: A systematic review and meta-analysis

Abstract

Background. 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.Objectives. 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. Materials and methods.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. Results.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 (I 2 = 85.29%, Q = 48. 15), which is attributable to the heterogeneity of the included studies.Conclusions. 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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Clinical Efficacy of Biomimetic Bioactive Biomaterials for Dental Pulp Capping: A Systematic Review and Meta-Analysis

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Automatic Assessment of Upper Extremity Function and Mobile Application for Self-Administered Stroke Rehabilitation

“…The use of various sensing equipment in the automated assessment of motor function in neurorehabilitation has recently been explored [23], [24], [25], and a description of representative sensors is provided in Table I. Joint tracking data from Kinect [26] have been used to evaluate the motor function of patients using machine learning algorithms [27], [28].…”