Journal Article

The diagnostic performance of machine learning based on resting-state functional magnetic resonance imaging data for major depressive disorders: a systematic review and meta-analysis

Zhao, Wei, Yi, Sijie, Chen, Yanjing, Liu, Jun

2023

Overview

Objective: Machine learning (ML) has been widely used to detect and evaluate major depressive disorder (MDD) using neuroimaging data, i.e., resting-state functional magnetic resonance (rs-fMRI). However, the diagnostic efficiency is unknown. The aim of the study is to conduct an updated meta-analysis to evaluate the diagnostic performance of ML based on rs‑fMRI data for MDD. Methods: English databases were searched for relevant studies. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to assess the methodological quality of the included studies. A random‑effects meta‑analytic model was implemented to investigate the diagnostic efficiency, including sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Regression meta-analysis and subgroup analysis were performed to investigate the cause of heterogeneity. Results: Thirty-one studies were included in this meta-analysis. The pooled sensitivity, specificity, DOR, and AUC with 95% confidence intervals were 0.80 (0.75, 0.83), 0.83 (0.74, 0.82), 14.00 (9, 22.00), and 0.86 (0.83, 0.89), respectively. There were significant heterogeneity among the included studies. The meta-regression showed that the leave-one-out cross-validation（loocv）: (sensitivity: p100: (sensitivity: p100:sensitivity:0.71, specificity: 0.72,n<100:sensitivity:0.81, specificity: 0.79), the different levels of disease evaluated by Hamilton Depression Rating Scale （HDRS） (mild vs moderate vs severe: sensitivity:0.52 vs 0.86 vs 0.89, specificity:0.62 vs 0.78 vs 0.82) ， the depression scales in the same symptom, (BDI vs HDRS/HAMD: sensitivity:0.86 vs 0.87, specificity: 0.78 vs 0.80),and the features(graph vs functional connectivity: sensitivity:0.84 vs 0.86,specificity:0.76 vs 0.78) selected might be the causes of heterogeneity. Conclusion: ML showed high accuracy for the automatic diagnosis of MDD. Future studies are warranted to promote the potential use of those classification algorithms to clinical settings.

• Publisher: Frontiers Research Foundation,Frontiers Media S.A
• Subject: Accuracy / Artificial intelligence / Biomarkers / Brain research / Contingency tables / depression / functional connectivity / Functional magnetic resonance imaging / functional MRI / Learning algorithms / Machine learning / Magnetic resonance imaging / Medical imaging / Mental depression / Mental disorders / Meta-analysis / Neural networks / Neuroimaging / Neuroscience / Quality control / Software / support vector machine