Association of Retinal Biomarkers With the Subtypes of Ischemic Stroke and an Automated Classification Model

PURPOSE. Retinal microvascular changes are associated with ischemic stroke, and optical coherence tomography angiography (OCTA) is a potential tool to reveal the retinal microvasculature. We investigated the feasibility of using the OCTA image to automatically identify ischemic stroke and its subtypes (i.e. lacunar and non–lacunar stroke), and exploited the association of retinal biomarkers with the subtypes of ischemic stroke. METHODS. Two cohorts were included in this study and a total of 1730 eyes from 865 participants were studied. A deep learning model was developed to discriminate the subjects with ischemic stroke from healthy controls and to distinguish the subtypes of ischemic stroke. We also extracted geometric parameters of the retinal microvasculature at different retinal layers to investigate the correlations. RESULTS. Superficial vascular plexus (SVP) yielded the highest areas under the receiver operating characteristic curve (AUCs) of 0.922 and 0.871 for the ischemic stroke detection and stroke subtypes classification, respectively. For external data validation, our model achieved an AUC of 0.822 and 0.766 for the ischemic stroke detection and stroke subtypes classification, respectively. When parameterizing the OCTA images, we showed individuals with ischemic strokes had increased SVP tortuosity (B = 0.085, 95% confidence interval [CI] = 0.005–0.166, P = 0.038) and reduced FAZ circularity (B = −0.212, 95% CI = −0.42 to −0.005, P = 0.045); non–lacunar stroke had reduced SVP FAZ circularity (P = 0.027) compared to lacunar stroke. CONCLUSIONS. Our study demonstrates the applicability of artificial intelligence (AI)–enhanced OCTA image analysis for ischemic stroke detection and its subtypes classification. Biomarkers from retinal OCTA images can provide useful information for clinical decision–making and diagnosis of ischemic stroke and its subtypes.