Oculomics approaches using retinal imaging to predict mental health disorders: a systematic review and meta-analysis

This meta-analysis evaluated the diagnostic performance of oculomics approaches, including deep learning, machine learning, and logistic regression models, in detecting major mental disorders using retinal imaging.

A systematic review identified 11 studies for inclusion. Study quality was assessed using the QUADAS-2 tool, revealing a high risk of bias, particularly in patient selection and index test design. Pooled sensitivity and specificity were estimated using random-effects models, and diagnostic performance was evaluated through a summary receiver operating characteristic curve.

The analysis included 13 diagnostic models across 11 studies, covering major depressive disorder, bipolar disorder, schizophrenia, obsessive-compulsive disorder, and autism spectrum disorder using color fundus photography, and optical coherence tomography (OCT), and OCT angiography. The pooled sensitivity was 0.89 (95% CI: 0.78-0.94), and specificity was 0.87 (95% CI: 0.74-0.95). The pooled area under the curve was 0.904, indicating high diagnostic accuracy. However, all studies exhibited a high risk of bias, primarily due to case-control study designs, lack of external validation, and selection bias in 77% of studies. Some models showed signs of overfitting, likely due to small sample sizes, insufficient validation, or dataset limitations. Additionally, no distinct retinal patterns specific to mental disorders were identified.

While oculomics demonstrates potential for detecting mental disorders through retinal imaging, significant methodological limitations, including high bias, overfitting risks, and the absence of disease-specific retinal biomarkers, limit its current clinical applicability. Future research should focus on large-scale, externally validated studies with prospective designs to establish reliable retinal markers for psychiatric diagnosis.