Predicting cerebral small vessel disease through retinal scans and demographic data with Bayesian feature selection

Abstract

Ophthalmic examinations, including fundus and optical coherence tomography (OCT) imaging, are integral in diagnosing systemic diseases affecting the eye, such as diabetic retinopathy. Recent studies have highlighted the related pathological features between retinal and cerebral small vessels, suggesting that retinal microvascular changes could reflect the status of cerebral small vessel disease (CSVD). In this research, we assessed patient's fundus and OCT images, with a focus on CSVD severity. By incorporating patient demographic data, we developed a sparse Bayesian-based model using retinal and fundus vascular imaging for CSVD diagnosis, achieving an accuracy (ACC) of 71.71% and an area under the curve (AUC) of 73.35%. Our findings indicate that ophthalmic examinations can be a cost-effective screening method for CSVD. Implementing this methodology in optician and eye clinics may substantially lessen societal and familial impacts relative to conventional magnetic resonance imaging (MRI) diagnostics. Additionally, this study identified crucial early warning signs of severe CSVD, such as global venous width, and provided valuable insights into the nature of the disease.