Retinal revelations: Seeing beyond the eye with artificial intelligence - John Davis Akkara (Kerala Journal of Ophthalmology)
Artificial intelligence (AI) has revolutionized ophthalmology by aiding in the diagnosis, prognosis, and treatment planning of various eye diseases. However, AI’s potential extends beyond ocular conditions. By analyzing eye-related biomarkers, AI can utilize the eye as a window into the body’s systemic health. This field, known as oculomics, leverages AI and deep learning algorithms to process vast amounts of data from imaging techniques such as fundus photography, optical coherence tomography (OCT), OCT angiography, infrared iris imaging, slit-lamp photography, and external eye photography. AI-powered analysis of these images can predict systemic diseases such as Alzheimer’s, Parkinson’s, cardiovascular disease, cerebrovascular disease, chronic kidney disease, and liver disease. Retinal changes —including alterations in the retinal nerve fiber layer, ganglion cell layer, and retinal vessels —serve as valuable indicators of these conditions. Additionally, AI can estimate age, sex, body composition, and other health parameters from eye images. While the potential of AI in oculomics is promising, challenges such as access to ophthalmic imaging, data quality, and the need for rigorous validation must be addressed to ensure its widespread adoption and clinical utility. Nevertheless, AI holds the potential to transform healthcare by enabling early detection, noninvasive screening, and personalized treatment for a wide range of systemic diseases.