SynthesisJournal of medical Internet research2025
The Role of Machine Learning in Cognitive Impairment in Parkinson Disease: Systematic Review and Meta-Analysis.
Synthesis in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- A Comparison of Machine Learning Models for Classification of Parkinson's Disease During a Working Memory and Sustained Attention Task.Brain sciences · 2026Article
- Observational
- Diagnostic classification of mild cognitive impairment in Parkinson's disease using subject-level stratified machine-learning analysis.Frontiers in aging neuroscience · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundParkinson disease (PD) is a common neurodegenerative disease characterized by both motor and nonmotor 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 patients with PD.
objectiveThis study aims to summarize different ML models applied to cognitive impairment in patients with PD and to identify determinants for improving diagnosis and predictive power for early detection of cognitive impairment.
methodsPubMed, Cochrane, Embase, and Web of Science were searched for relevant articles on March 2, 2024. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Bivariate meta-analysis was used to estimate pooled sensitivity and specificity results, presented as odds ratio (OR) and 95% CI. A summary receiver operator characteristic (SROC) curve was used.
resultsA total of 38 articles met the criteria, involving 8564 patients with PD and 1134 healthy controls. Overall, 120 models reported sensitivity and specificity, with mean values of 71.07% (SD 13.72%) and 77.01% (SD 14.31%), respectively. Predictors commonly used in ML models included clinical features, neuroimaging features, and other variables. No significant heterogeneity was observed in the bivariate meta-analysis, which included 12 studies. Using sensitivity as the metric, the combined sensitivity and specificity were 0.76 (95% CI 0.67-0.83) and 0.83 (95% CI 0.76-0.88), respectively. When specificity was used, the combined values were 0.77 (95% CI 0.65-0.86) and 0.76 (95% CI 0.63-0.85), respectively. The area under the curves of the SROC were 0.87 (95% CI 0.83-0.89) and 0.83 (95% CI 0.80-0.86) respectively.
conclusionsOur 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.
trial registrationPROSPERO CRD42023480196; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023480196.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.