Cornea Oculomics: A Clinical Blueprint for Extending Corneal Diagnostics and Artificial Intelligence in Systemic Health Insights

Abstract

Oculomics is an emerging field that leverages ophthalmic imaging data to identify biomarkers of systemic disease, facilitating early diagnosis and risk stratification. Despite its growing recognition, gaps remain in the literature regarding the clinical applications of oculomics. Various systemic diseases—including metabolic disorders (e.g., diabetes mellitus), infectious diseases (e.g., COVID-19), neurodegenerative diseases (e.g., dementia), hematologic disorders (e.g., thalassemia), autoimmune conditions (e.g., rheumatoid arthritis), and genetic syndromes (e.g., Fabry disease)—exhibit ocular manifestations detectable through in vivo confocal microscopy and anterior segment optical coherence tomography, among other imaging modalities. Increasing evidence supports the role of corneal imaging in identifying systemic disease biomarkers, a process further enhanced by artificial intelligence-driven analyses. This review synthesizes the current findings on corneal biomarkers of systemic disease, their ophthalmic imaging correlates, and the expanding role of corneal oculomics in translational medicine. Additionally, we explore future directions for integrating oculomics into clinical practice and biomedical research.