  We investigated the potential of optical coherence tomography (OCT) as an
additional imaging technique to predict future cardiovascular disease (CVD). We
utilised a self-supervised deep learning approach based on Variational
Autoencoders (VAE) to learn low-dimensional representations of high-dimensional
3D OCT images and to capture distinct characteristics of different retinal
layers within the OCT image. A Random Forest (RF) classifier was subsequently
trained using the learned latent features and participant demographic and
clinical data, to differentiate between patients at risk of CVD events (MI or
stroke) and non-CVD cases. Our predictive model, trained on multimodal data,
was assessed based on its ability to correctly identify individuals likely to
suffer from a CVD event(MI or stroke), within a 5-year interval after image
acquisition. Our self-supervised VAE feature selection and multimodal Random
Forest classifier differentiate between patients at risk of future CVD events
and the control group with an AUC of 0.75, outperforming the clinically
established QRISK3 score (AUC= 0.597). The choroidal layer visible in OCT
images was identified as an important predictor of future CVD events using a
novel approach to model explanability. Retinal OCT imaging provides a
cost-effective and non-invasive alternative to predict the risk of
cardiovascular disease and is readily accessible in optometry practices and
hospitals.
