Abstract
Presentation Description :
In ophthalmologic practice, retinal images are routinely obtained to diagnose and monitor primary eye diseases and systemic conditions affecting the eye, such as diabetic retinopathy. Recent studies have shown that biomarkers on retinal images, for example retinal blood vessel density or tortuosity, are associated with cardiac function and may identify patients at risk of coronary artery disease. In this work, we investigate the use of retinal images, alongside relevant patient metadata, to estimate left ventricular mass and left ventricular end-diastolic volume, and subsequently, predict incident myocardial infarction. We trained a multichannel variational autoencoder and a deep regressor model to estimate left ventricular mass (4.4 (-32.30, 41.1) g) and left ventricular end-diastolic volume (3.02 (-53.45, 59.49) ml) and predict risk of myocardial infarction (AUC = 0.80+/-0.02, sensitivity = 0.74+/-0.02, specificity =0.71+/-0.03) using just the retinal images and demographic data. Our results indicate that patients are at elevated risk of future myocardial infarction, which might be identified from retinal imaging available in every optician and eye clinic.
The work to be presented is pending a patent, and technology is under commercial licensing negotiations with two independent companies.
This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.