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

Abstract

Objective: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: Machine learning; Mental disorders; Oculomics; Retinal imaging; Selection bias.