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Toward neuroimaging-based diagnostic support: a deep learning approach with a closed-loop system for psychiatric

Qingfeng Li¹, Wengzheng Wang¹, Qian Guo¹

¹Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.

|December 16, 2025

Summary

A new deep learning model, the patch-based hierarchical network (PHN), accurately classifies psychiatric disorders using structural MRI scans. This AI tool shows promise for integrating neuroimaging into clinical practice for objective diagnostic support.

Keywords:

artificial intelligence neuroscience

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Area of Science:

Neuroimaging
Artificial Intelligence
Psychiatric Diagnostics

Background:

Neuroimaging provides insights into psychiatric disorders but faces challenges in clinical translation.
Current diagnostic methods for psychiatric disorders can be subjective.

Purpose of the Study:

To develop and validate a deep learning framework for classifying multiple psychiatric disorders using structural MRI.
To assess the generalizability and real-world applicability of the developed model.
To integrate the model into a clinical workflow for diagnostic support.

Main Methods:

Development of the patch-based hierarchical network (PHN), a deep learning framework.
Training the PHN on a large dataset (n=2,490) including four major psychiatric disorders and controls.
Validation on independent research datasets (n=1,346) and real-world clinical data (n=344).

Main Results:

The PHN demonstrated robust performance in classifying psychiatric disorders.
The model showed generalizability across diverse datasets, including real-world clinical data.
The system successfully reflected complex clinical presentations, such as comorbidities.

Conclusions:

The patch-based hierarchical network (PHN) offers a promising approach for objective psychiatric diagnosis using structural MRI.
Integration into a clinical workflow represents a significant step towards bridging the research-to-practice gap in psychiatric neuroimaging.
AI-powered neuroimaging analysis can provide valuable objective support for clinicians in diagnosing psychiatric disorders.