JMIR Publications

ABSTRACT

Background:

Parkinson’s disease (PD) is a common neurodegenerative disease characterized by both motor and non-motor symptoms. Cognitive impairment often occurs early in the disease and can persist throughout its progression, severely impacting patients’ quality of life. The utilization of machine learning (ML) has recently shown promise in identifying cognitive impairment in PD patients.

Objective:

This study aims to summarize different ML models applied to cognitive impairment in PD patients and to identify determinants for improving the diagnosis and predictive power to find cognitive impairment at an early stage.

Methods:

PubMed, Cochrane, Embase, and Web of Science were searched for relevant articles were conducted on March 2, 2024. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Bivariate meta-analysis was used to estimate the pooled results for sensitivity and specificity.

Results:

A total of 43 articles met the criteria, involving 9,139 PD patients and 1,353 healthy controls. A total of 151 models were analyzed, with an accuracy ranging from 60% to 90%. Predictors commonly used in ML models included clinical features, neuroimaging features, and other variables. In the bivariate meta-analysis, including only 12 studies, no significant heterogeneity was observed.

Conclusions:

Our findings provide a comprehensive summary of various ML models and demonstrate the effectiveness of ML as a tool for diagnosing and predicting cognitive impairment in patients with PD.