Validated through a multi-tier framework across 213,762 images from 12 multiethnic cohorts (China, Singapore, UK, US, Denmark, Malaysia), DeepRETStroke achieved exceptional performance (Table 1). For SBI detection, the fundus model significantly outperformed clinical metadata models, with internal AUCs of 0.797 versus 0.633, and external AUCs of 0.751-0.792 versus 0.537-0.726, reaching sensitivity/specificity of 0.775/0.824 in external cohorts. Incident stroke prediction attained an internal AUC of 0.901 (0.846-0.940) and external AUCs of 0.728-0.895, maintaining robustness over 5 years across diabetic/hypertensive subgroups. Recurrent stroke prediction showed internal/external AUCs of 0.769/0.727 for the fundus model, surpassing metadata models (0.568/0.705), with the performance gap reflecting heightened complexity in patients with prior cerebrovascular damage3,12. In a prospective study of 218 Chinese adults with prior stroke/SBI, DeepRETStroke-guided integrated management (IM) demonstrated real-world impact. Non-IM patients stratified as high-risk by the fundus model had 202.17 recurrent strokes/1000 person-years, while fundus-identified low-risk patients showed significantly fewer events versus metadata-based stratification. DeepRETStroke drove an 82.44% relative reduction in recurrent strokes versus conventional screening (preliminary, small sample size). This performance surpassed traditional risk models, positioning retinal imaging as a scalable window into cerebrovascular risk trajectory.
However, despite DeepRETStroke's promising clinical potential, unresolved challenges limit its applicability: (1) Generalizability limitations: The model's heavy reliance on Chinese cohorts (due to scarce global paired retinal-brain imaging datasets) risks underrepresenting genetic, environmental, and healthcare disparities across populations. This may introduce biases in AI-driven stroke prediction, particularly for African, Indigenous, or admixed populations, where cerebrovascular phenotypes may differ. To address generalizability limitations rooted in Chinese cohort dependencies, potential solutions include implementing FAIR-compliant federated learning with multi-ethnic data partnerships, coupled with ethnicity-specific adaptive modules to enhance model performance across diverse populations. This enables clinics across Africa, Indigenous communities, and admixed populations to refine DeepRETStroke without sharing raw data, with ongoing validation in 10,000 underrepresented participants targeting minimal performance variance across ethnic groups. (2) Oversimplified SBI phenotyping: Treating silent infarctions as binary (present/absent) ignores critical heterogeneity. Lacunar infarcts are typically subcortical and of small-vessel origin, versus cortical SBIs carry divergent stroke recurrence risks, yet retinal signatures for these subtypes remain uncharacterized. Future iterations could develop an open-source SBI atlas combining retinal topography with 3D MRI lesion mapping to enable AI-driven subclassification. Achieving this requires overcoming computational hurdles in cross-modal registration, such as aligning MRI voxel-level annotations (lesion load/spatial distribution) with microvascular retinal features. (3) Unresolved biological plausibility: While gradient-based visualizations highlight salient retinal regions, causal relationships between specific vasculature patterns, such as altered fractal dimensions or arteriolar narrowing and cerebrovascular event,s remain speculative. Retinal changes may partly reflect systemic comorbidities (e.g., hypertension) rather than direct neurovascular injury correlates, a concern substantiated by observed discordance of some diabetic patients showing retinal pathology without corresponding silent brain infarctions. This discrepancy suggests retinal alterations may reflect either early cerebrovascular dysfunction preceding infarction or systemic confounders. To definitively validate the "eye-brain window" hypothesis, histopathological validation remains essential, leveraging cross-modal analysis that correlates in vivo retinal patterns with post-mortem cerebral microvasculature to map microinfarcts to specific arteriolar abnormalities. This can potentially integrate the following methodologies: counterfactual models isolating retinal biomarkers from systemic comorbidities like hypertension through perturbation analysis; causal scoring quantifying neurovascular linkages against histopathological evidence; and dynamic angiography capturing real-time hemodynamic precursors such as altered flow velocities preceding clinically evident infarction.