Machine-Learning Classification of Schizophrenia Using Automated Retinal-Imaging Analysis: A Pilot Study

Background: Schizophrenia is a debilitating disorder that exacts a heavy burden on sufferers and carers alike. Despite improvements in neuroimaging techniques in the past decades, schizophrenia has lacked a clear definition at the neuroanatomical level. The presence of diagnostic markers may lead to breakthroughs with our current phenomenological approach to schizophrenia, assisting with diagnosis and monitoring of the disease. Prior study findings suggest structural retinal changes in schizophrenia, including retinal-layer thinning and microvascular changes. Aim: We aim to evaluate whether retinal imaging, in combination with machine-learning approaches, can facilitate diagnostic classification of schizophrenia. Furthermore, we characterise retinal structural features in individuals with schizophrenia compared with healthy controls, and examine whether these retinal measures are associated with disease status, clinical severity, functional outcomes, and other key clinical variables. Method: This study recruited 64 individuals with schizophrenia and 64 healthy controls. Participants in the schizophrenia group completed standardized clinical rating scales to assess symptom severity and functional status. Additional clinical variables, including demographic characteristics, duration of untreated psychosis, and antipsychotic medication dosage, were recorded. All participants underwent fundus photography, and retinal images were processed using the Automated Retinal-Imaging Analysis (ARIA) system developed at the Chinese University of Hong Kong (CUHK). Extracted retinal parameters were used to train machine-learning models to classify schizophrenia cases versus controls. Using a cross-sectional design, we compared retinal characteristics between groups while adjusting for relevant confounders, and evaluated associations between retinal measures and clinical variables within the schizophrenia cohort. Results: Our machine-learning model successfully distinguished individuals with schizophrenia from healthy controls, achieving high sensitivity (96.9%) and specificity (92.2%). Consistent with prior literature, we observed reductions in some measures of retinal thickness in the schizophrenia group, along with some novel alterations in venular microvascular structure. No associations between retinal measurements and symptom severity or illness-related variables were statistically significant. Conclusions: The strong discriminatory performance of our machine-learning (ML) model underscores the potential value of ML-based retinal analysis as an adjunct to existing diagnostic and risk-stratification approaches for schizophrenia. Our findings suggest retinal-layer alterations in schizophrenia and novel microvascular changes that have not been examined in earlier work. Together, these results highlight promising directions for future large-scale investigations into retinal biomarkers in schizophrenia.