Potential Clinical Applicability of Deep Learning in the Diagnosis of Major Depressive Disorder Using rs-fMRI: A Systematic Literature Review

Background

Major depressive disorder (MDD) is a leading cause of disability worldwide. Although deep learning has shown promise in identifying brain patterns associated with depression, challenges remain regarding data variability, interpretability, and clinical application.

Research

This systematic review analysed deep learning approaches for MDD detection using resting-state functional MRI (rs-fMRI). The study examined commonly identified brain regions, model performance, generalisability, and limitations of current approaches.

Potential Impact
 The review highlights opportunities for improving AI-based depression diagnosis through multi-centre datasets, better validation strategies, and more interpretable models suitable for clinical use.