Detection of Systemic Cardiovascular Illnesses and Cardiometabolic Risk Factors with Machine Learning and Optical Coherence Tomography Angiography: a Pilot Study

Background/objectives: Optical coherence tomography angiography (OCTA) has been found to identify changes in the retinal microvasculature of people with various cardiometabolic factors. Machine learning has previously been applied within ophthalmic imaging but has not yet been applied to these risk factors. The study aims to assess the feasibility of predicting the presence or absence of cardiovascular conditions and their associated risk factors using machine learning and OCTA.

Methods: Cross-sectional study. Demographic and co-morbidity data was collected for each participant undergoing 3 × 3 mm, 6 × 6 mm and 8 × 8 mm OCTA scanning using the Carl Zeiss CIRRUS HD-OCT model 5000. The data was then pre-processed and randomly split into training and testing datasets (75%/25% split) before being applied to two models (Convolutional Neural Network and MoblieNetV2). Once developed on the training dataset, their performance was assessed on the unseen test dataset.

Results: Two hundred forty-seven participants were included. Both models performed best in predicting the presence of hyperlipidaemia in 3 × 3 mm scans with an AUC of 0.74 and 0.81, and accuracy of 0.79 for CNN and MobileNetV2 respectively. Modest performance was achieved in the identification of diabetes mellitus, hypertension and congestive heart failure in 3 × 3 mm scans (all with AUC and accuracy >0.5). There was no significant recognition for 6 × 6 and 8 × 8 mm for any cardiometabolic risk factor.

Conclusion: This study demonstrates the strength of ML to identify the presence cardiometabolic factors, in particular hyperlipidaemia, in high-resolution 3 × 3 mm OCTA scans. Early detection of risk factors prior to a clinically significant event, will assist in preventing adverse outcomes for people.