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Diagnosing schizophrenia using deep learning: Novel interpretation approaches and multi-site validation.

Tingting Weng¹, Yuemei Zheng², Yingying Xie³

¹School of Medical Imaging, Tianjin Medical University, Tianjin 300203, China.

|March 21, 2024

Summary

Deep learning models show promise for diagnosing schizophrenia, achieving 82% accuracy across multiple international centers. This approach enhances diagnostic reliability and identifies key brain region differences in schizophrenia patients.

Keywords:

Deep learning Interpretation Schizophrenia

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Area of Science:

Neuroscience
Artificial Intelligence
Psychiatry

Background:

Schizophrenia significantly impacts individuals and society.
Deep learning (DL) offers potential for objective schizophrenia diagnosis by analyzing complex patterns.
Current DL models lack interpretability and generalizability due to single-center data limitations.

Purpose of the Study:

To develop and validate a generalizable deep learning model for schizophrenia diagnosis using multi-center data.
To improve the clinical utility of DL in psychiatry through enhanced interpretability.
To identify neuroanatomical differences associated with schizophrenia.

Main Methods:

Utilized a 3D Resnet model trained and tested across nine global centers.
Employed a leave-one-center-out validation strategy to assess model generalizability.
Applied SHapley Additive exPlanations (SHAP) with an anatomical atlas for neuroanatomical interpretation.

Main Results:

Achieved 82% classification performance (area under the curve) across diverse international datasets.
Identified significant differences in the thalamus, pallidum, and inferior frontal gyrus between schizophrenia patients and healthy controls.
Refined SHAP methods provided precise neuroanatomical and functional interpretations.

Conclusions:

Multi-center validation enhances the reliability and generalizability of DL models for schizophrenia diagnosis.
The study successfully linked DL findings to known neurobiological differences in schizophrenia.
Interpretable DL models hold significant potential for clinical application in psychiatric diagnostics.