Deep learning for early detection of cerebral small vessel disease using self-supervised graph embeddings and retinal image analysis

The primary driver or cause of cognitive decline and stroke is Cerebral Small Vessel Disease (CSVD), which currently requires neuroimaging tests, which are expensive to obtain and inaccessible in standard clinical settings. Low-cost retinal imaging techniques offer non-invasive assessments that mirror the condition of the brain’s small blood vessels (cerebral microvasculature). State-of-the-art diagnostic methods currently have no accessible, non-invasive, or cost-effective solution to identify CSVD at its earliest stages through visual assessment of retinal biomarkers. This study presents the Retino-Neuro Vision Transformer (RNV-T) framework as a proposed method to detect CSVD utilizing multimodality retinal imaging. The model system comprises five fundamental phases, beginning with Local Vascular Extraction (LVE), followed by Global Transformer-based Encoding (GTE), then proceeding to Graph-Based Relational Learning through Graph-based Convolutional Attention Network (G-CAN) before implementing Local-Global Attention Fusion (LGAF) as well as Optimized training procedures to obtain precise micro-vascular abnormality detection. The diagnostic performance of this model reaches 98.8% accuracy and shows 97.4% sensitivity along with 98.1% specificity, surpassing previous detection approaches. This diagnostic system represents a major leap forward in neuro-ophthalmic care because it enables early prediction of CSVD while expanding medical accessibility through retinal scans that are easy to conduct.