# Diagnostic Accuracy of Machine Learning-Assisted MRI for Mild Cognitive Impairment in Parkinson's Disease: A Systematic Review and Meta-Analysis

## TL;DR

This study reviews and analyzes how well machine learning combined with MRI can detect mild cognitive impairment in Parkinson's disease patients.

## Contribution

The paper provides a meta-analysis of machine learning-assisted MRI's diagnostic accuracy for PD-MCI.

## Key findings

- Pooled sensitivity and specificity for diagnosing PD-MCI were 82% and 81%, respectively.
- The area under the SROC curve was 0.85, indicating high diagnostic accuracy.
- Studies showed low risk of bias and no significant publication bias.

## 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 (p=0.30). In summary, the MRI combined with machine learning for diagnosing PD-MCI achieved high accuracy with the pooled sensitivity of 82% and specificity of 81%.