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

TITLE=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

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1174080

DOI=10.3389/fnins.2023.1174080

ISSN=1662-453X

ABSTRACT=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: p<0.01, specificity: p<0.001), graph theory: (sensitivity: p<0.05, specificity: p<0.01),
n>100: (sensitivity: p<0.001, specificity: p<0.001)、simens equipment：(sensitivity: p<0.01, 
specificity : p<0.001), 3.0T field strength：(sensitivity: p<0.001, specificity: p=0.04) , Beck 
Depression Inventory（BDI）: (sensitivity: p=0.04, specificity: p=0.06) might be the sources of 
heterogeneity,while The subgroup analysis showed that the sample size (n>100: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.