SYSTEMATIC REVIEW article
Front. Aging Neurosci.
Sec. Parkinson’s Disease and Aging-related Movement Disorders
This article is part of the Research TopicAI-Enhanced Biomarkers: Revolutionizing Early Detection and Precision Medicine in NeurodegenerationView all 7 articles
Machine Learning Methods for the Detection and Prediction of Cognitive Impairment in Parkinson's Disease: A Systematic Review and Meta-Analysis
Provisionally accepted
- 1The Second Affiliated Hospital, Xinjiang Medical University, Urumqi County Hospital of Xinjiang, Urumqi, China
- 2School of Nursing, Xinjiang Medical University, Urumqi, China
- 3Xinjiang Medical University, Urumqi, China
- 4The Sixth Affiliated Hospital of Xinjiang Medical University, Urumqi, China
- 5Department of Emergency, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
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Background Cognitive impairment in Parkinson's disease (PD-CI) is a prevalent non-motor symptom, significantly diminishing quality of life and imposing a substantial family burden. Effective predictive tools are currently scarce, and the diagnostic pathway is intricate. With the growing use of artificial intelligence in healthcare, machine learning (ML) methodologies have been explored for the diagnosis and early risk prediction of PD-CI; however, their efficacy and accuracy necessitate systematic evaluation. Consequently, this investigation undertook a systematic review and meta-analysis. Method A comprehensive literature retrieval was conducted across Web of Science, PubMed, Embase, and Cochrane Library, encompassing studies published from database inception to August 10, 2025. The PROBAST tool facilitated quality appraisal, ultimately incorporating 52 publications, of which 25 addressed diagnosis and 27 focused on risk prediction. Results Findings indicated that within the validation cohorts, ML models for PD-CI diagnosis achieved a c-index of 0.82, with a sensitivity of 0.57 and specificity of 0.77. For PD-CI risk prediction, the c-index reached 0.83, accompanied by a sensitivity of 0.77 and specificity of 0.76. These results suggest that ML exhibits considerable accuracy in both the diagnosis and risk prediction of PD-CI. The models primarily incorporated variables such as clinical data, genetic characteristics, biomarkers, neuroimaging, and radiomics, and no overt signs of overfitting were detected. Conclusion This research provides an evidence-based foundation for the future development of PD-CI risk prediction and intelligent diagnostic tools, thereby promoting the advancement and application of ML within Parkinson's disease and related domains.
Keywords: Parkinson's disease, cognitive dysfunction, Meta-analysis, calculation and diagnostic accuracy, Systematic review, machine learning
Received: 12 Sep 2025; Accepted: 27 Nov 2025.
Copyright: © 2025 Jiang, Yang, Wang, Jiang and Jiang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xinling Yang
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