Computer vision-based classification of schizophrenia patients from retinal imagery

Abstract

Changes in retinal structure have been documented in patients with chronic schizophrenia using optical coherence tomography (OCT) metrics, but these studies were limited by the measurements provided by OCT machines. In this paper, we leverage machine and deep learning techniques to analyze OCT images and train algorithms to differentiate between schizophrenia patients and healthy controls. In order to address data scarcity issues, we use intermediate representations extracted from ReLayNet, a pretrained convolutional neural network designed to segment macula layers from OCT images. Experimental results show that classifiers trained on deep features and OCT-machine provided metrics can reliably distinguish between chronic schizophrenia patients and an age-matched control population. Further, we present what is to our knowledge the first reported empirical evidence showing that separation can be achieved between first-episode schizophrenia patients and their age- matched control group by leveraging deep image features extracted from OCT imagery.