Machine Learning for Parkinson’s Disease Biomarkers: A Systematic Review of Diagnostic Performance, Risk of Bias, and Clinical Translation Readiness
Abstract
Background/Objectives: Machine learning (ML) is frequently proposed for Parkinson’s disease (PD) diagnosis, but reported accuracy may not reflect methodological quality, generalizability, or clinical feasibility. We compared diagnostic performance, risk of bias, external validation, and deployment burden across biomarker modalities. Methods: We searched PubMed/Medical Literature Analysis and Retrieval System Online (MEDLINE, Web of Science, and Institute of Electrical and Electronics Engineers (IEEE) Xplore for studies from January 2020 through March 2026 using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020-compliant protocol registered with the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY). AI-assisted screening was audited in 147 records (Cohen’s κ = 0.862). Reviewers verified extracted data and Risk Of Bias ASsessment Tool (PROBAST) judgments. Deployment burden was rated across five acquisition domains. Full-text assessment used a modality-stratified sample of retained records; we audited those not selected. Results: Of 8135 deduplicated records, 2046 were retained after screening and 377 reports were sampled for full-text assessment; the final synthesis included 264 studies across nine modalities. Among 252 studies reporting accuracy, median best-model accuracy was 95.3% (range 64.9–100%). However, under our adapted PROBAST rules, 259 studies (98.1%) were at high overall risk of bias; 215…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
Read the paper
Related studies
- Global, regional, and national burden of hepatitis B, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019
- A meta‐analysis of anal cancer incidence by risk group: Toward a unified anal cancer risk scale
- Global fertility in 204 countries and territories, 1950–2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021
- Exercise interventions in Alzheimer's disease: A systematic review and meta-analysis of randomized controlled trials
- Prognostic Significance of Carbonic Anhydrase IX Expression in Cancer Patients: A Meta-Analysis
- Glucocorticoid Resistance: Is It a Requisite for Increased Cytokine Production in Depression? A Systematic Review and Meta-Analysis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.