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Schizophrenia, a severe psychiatric disorder, arises from a complex interplay of biological factors, including genetic predisposition, structural brain abnormalities, neurotransmitter dysregulation, and developmental irregularities. These factors collectively contribute to the onset and progression of the disorder, which typically manifests in late adolescence or early adulthood.
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Machine learning algorithms and artificial neural networks for predicting schizophrenia using orbital parameters.

Elif Emre¹, Derya Ozturk Soylemez², Yusuf Secgin³

¹Department of Anatomy, Faculty of Medicine, Firat University, Elazig, Turkey.

Scientific Reports

|November 29, 2025

Summary

Machine learning and artificial neural networks show promise in diagnosing schizophrenia by analyzing orbital measurements from CT scans. This AI-driven approach could offer new tools for early detection of this persistent mental illness.

Keywords:

Artificial neural networks Computed tomography Machine learning Orbital parameters Schizophrenia

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Area of Science:

Neuroimaging
Artificial Intelligence in Psychiatry
Medical Diagnostics

Background:

Schizophrenia is a complex mental illness with unclear origins.
Current diagnostic methods can be lengthy and subjective.
Investigating novel biomarkers for schizophrenia is crucial.

Purpose of the Study:

To explore the potential of using computed tomography (CT) scans of the orbit to diagnose schizophrenia.
To apply artificial neural networks (ANNs) and machine learning (ML) algorithms for schizophrenia detection based on orbital morphometry.

Main Methods:

Retrospective analysis of CT scans from 180 individuals (90 healthy, 90 with schizophrenia).
Measurement of various orbital and skull parameters, including orbital width, aperture area, and optic nerve sheath width.
Application of ML algorithms (e.g., Extra Tree Classifier) and ANNs (e.g., Multilayer Perceptron Classifier) for classification.

Main Results:

Significant differences in orbital and skull measurements were found between healthy individuals and those with schizophrenia.
The Extra Tree Classifier achieved the highest accuracy (0.78), and the Multilayer Perceptron Classifier achieved 0.75.
Left orbital width was identified as the most influential feature for diagnosis using the SHAP analyzer.

Conclusions:

AI models analyzing orbital morphometry show potential as diagnostic tools for schizophrenia.
Orbital measurements derived from CT scans can serve as objective biomarkers for schizophrenia.
This study highlights the growing role of machine learning in psychiatric diagnostics.