Deep learning detection of subclinical cardiovascular dysfunction in type 2 diabetes through retinal photography

Abstract

Abstract Background Type 2 diabetes (T2D) is associated with excess atherosclerotic coronary disease and heart failure risk, with several cardiac abnormalities described in asymptomatic patients. Routine digital retinal photography performed for screening of diabetic retinopathy (DR) has the potential to identify patients with underlying cardiovascular disease (CVD). We aimed to determine whether features extracted from digital retinal photographs could detect subclinical cardiovascular dysfunction in asymptomatic people with T2D. Methods We prospectively enrolled adults with T2D and no history or symptoms of CVD to undergo extensive phenotyping with multiparametric stress perfusion cardiac MRI and CT coronary artery calcium scoring. Digital retinal photographs acquired for routine diabetic eye screening and performed within one year of cardiac evaluation were interrogated for retinopathy grading and using four deep learning (DL) models (Xception, Inception, EfficientNet, and MobileNet) to capture complex patterns within the retinal vasculature. Each subject had four retinal photographs (two of each eye) for DL analysis. Cardiac image analysis was performed blinded to participant details and independent of retinal images. Subjects with and without DR were compared. Participants were divided into training (80%) and validation datasets (20%) prior to DL analysis. DL models employed to predict from retinal images: presence or absence of coronary artery calcium on non-contrast CT, myocardial perfusion reserve <2.5, left ventricular (LV) global longitudinal strain <16%, absolute stress and rest myocardial blood flood (mL/g/min) used as continuous variables. AUC was used to assess the ability of DL models to discriminate between those without and without subclinical CVD. Results An initial 254 subjects (comprising 1,016 retinal photographs) were included in the analysis: 212 (83%) with no DR, 42 (17%) with grade 1 (mild background) DR. Key cardiac imaging parameters in subjects with and without DR are displayed in Table 1. The groups were well matched for age, sex, body mass index and blood pressure. Overall, those with grade 1 DR had higher atheroma burden, evidence of increased LV filling pressures (E/e’) and poorer systolic function, but no differences in stress and rest myocardial blood flow or myocardial perfusion reserve. However, all four DL models failed to achieve sufficient discrimination of any of the evaluated cardiac imaging parameters, with the majority achieving AUC scores below 50% for all cardiac parameters. Conclusion Asymptomatic adults with grade 1 DR exhibit more subclinical atherosclerosis and cardiovascular dysfunction than those without DR. However, DL transfer models could not discriminate between subjects with and without cardiovascular disease. Further work is needed to better define whether retinal screening may be used to identify those people with T2D most at risk of underlying cardiovascular disease.Subjects demographics