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Using Retinal Imaging to Study Dementia

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Deep learning model using retinal vascular images for classifying schizophrenia.

Abhishek Appaji¹, Vaishak Harish¹, Vittal Korann²

¹Department of Medical Electronics Engineering, B.M.S. College of Engineering, Bangalore, India.

Schizophrenia Research

|February 17, 2022

Summary

This summary is machine-generated.

Objective diagnosis of schizophrenia (SCZ) may be possible using retinal images. A deep learning model achieved 95% accuracy in detecting SCZ from fundus photographs, suggesting potential for clinical use.

Keywords:

Artificial intelligence Biomarker Fundus Psychosis Retina

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Area of Science:

Ophthalmology
Psychiatry
Artificial Intelligence

Background:

Current psychiatric diagnosis relies on subjective patient reports.
Objective diagnostic markers are needed to differentiate schizophrenia patients from healthy individuals.
Previous studies suggest retinal vascular abnormalities in schizophrenia patients.

Purpose of the Study:

To develop a deep learning model for detecting schizophrenia (SCZ) using retinal fundus images.
To assess the accuracy of a convolution neural network (CNN) in classifying SCZ patients and healthy volunteers.
To explore the potential of retinal images as objective biomarkers for schizophrenia.

Main Methods:

Recruited 327 subjects (139 SCZ patients, 188 healthy volunteers).
Acquired retinal fundus images using a fundus camera.
Preprocessed images and used a CNN for classification, evaluating performance with AUC.

Main Results:

The CNN model achieved 95% accuracy in classifying SCZ and healthy volunteers.
The model demonstrated a high diagnostic performance with an AUC of 0.98.
Retinal fundus images show potential for objective schizophrenia detection.

Conclusions:

Deep learning applied to retinal images shows promise for classifying schizophrenia.
This approach could assist clinicians in diagnosing schizophrenia.
Future research with larger cohorts is warranted to validate these findings.