Evaluating Retinal Blood Vessels for Predicting White Matter Hyperintensities in Ischemic Stroke: A Deep Learning Approach

Abstract

This study aims to investigate whether a deep learning approach incorporating retinal blood vessels can effectively identify ischemic stroke patients with a high burden of White Matter Hyperintensities (WMH) using Nuclear Magnetic Resonance Imaging (MRI) as the gold standard. In this cross-sectional study, we evaluated 263 ischemic stroke inpatients who had acquired both retinal fundus images and MRI images. The primary outcome was the diagnostic WMH on MRI brain, defined as different degrees of the age-related white matter changes (ARWMC) grade (<2 or ≥2). We developed a deep-learning network model with retinal fundus images to estimate WMH. The mean age of the patient cohort was 60.8 years, with 196 individuals (74.5%) being male. The prevalence of risk factors was as follows: hypertension in 237 (90.1%), diabetes in 109 (41.4%), hyperlipidemias in 84 (31.9%), coronary heart disease in 37 (14.1%), hyperhomocysteinemia in 70 (26.6%), and hyperuricemia in 73 (27.8%). Severe WMH defined as global ARWMC grade ≥2 was found in 139 (52.9%) participants. Using binocular fundus images, we achieved an F1 score of 0.811 and a Macro Accuracy of 0.811 in the ARWMC classification task. Additionally, we conducted experiments by progressively occluding fundus images to assess the relationship between different areas of the fundus images and ARWMC prediction. Our study presents a novel deep learning model designed to detect a high burden of WMH using binocular fundus images in ischemic stroke patients. We have conducted initial investigations into the predictive significance of various fundus image areas for WMH identification. These findings underscore the need for broader data collection, further model training, and prospective data validation.