ReviewParkinson's disease2025
Diagnostic Accuracy of Machine Learning-Assisted MRI for Mild Cognitive Impairment in Parkinson's Disease: A Systematic Review and Meta-Analysis.
Review in Parkinson's disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To evaluate the diagnostic accuracy of machine learning-assisted magnetic resonance imaging (MRI) in detecting cognitive impairment among Parkinson's disease (PD) patients through a systematic review and meta-analysis. We systematically searched for studies that applied machine learning algorithms to MRI data for diagnosing PD with mild cognitive impairment (PD-MCI). Data were extracted and synthesized to calculate pooled sensitivity, specificity, positive likelihood ratio (PLR) and negative diagnostic likelihood ratio (NLR), and diagnostic odds ratios (DOR). A bivariate random-effects model and summary receiver operating characteristic (SROC) curves were employed for statistical analysis. The quality of studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) instrument. The publication bias was investigated through Deeks' funnel plot. All statistical analyses were conducted using Stata 14.0. The pooled sensitivity and specificity for diagnosing PD-MCI using machine learning-assisted MRI were 0.82 (95% CI: 0.75-0.87) and 0.81 (95% CI: 0.73-0.87), respectively. The PLR was 4.28 (95% CI: 2.93-6.27), and the NLR was 0.23 (95% CI: 0.16-0.32), indicating a high diagnostic accuracy. The area under the curve (AUC) for the SROC was 0.85 (95% CI: 0.82-0.88). Quality assessment using the QUADAS-2 tool showed a predominantly low risk of bias among the studies, and the Deeks' funnel plot suggested no significant publication bias (
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