Fig. 1: Architecture and development pipeline of the DeepRETStroke AI system.
From: From retina to brain: how deep learning closes the gap in silent stroke screening
a Schematic overview of the DeepRETStroke system. The model encodes a domain-specific foundation representing eye-brain connections, enabling downstream applications such as silent brain infarctions (SBI) detection and future stroke prediction. EHR: electronic health record. b Three-stage pretraining and fine-tuning workflow: Stage 1 (Self-supervised pretraining): The retinal image encoder (adapted from RETFound) learns general features from unlabeled images in the SIM and CNDCS datasets. Stage 2 (Incident stroke prediction): The encoder and Stroke Predictor are trained on the SDPP dataset to forecast stroke risk. Stage 3 (Semi-supervised SBI detection): The SBI Detector is trained on limited MRI-labeled data (SDPP-MRI), then generates soft labels, pseudo-probability distributions rather than binary classifications, for the unlabeled SDPP images. Joint training integrates SBI soft labels with actual stroke outcome data to refine the encoder, SBI Detector, and Stroke Predictor. The pretrained system is fine-tuned on NDSP data (patients with prior stroke) for recurrent stroke prediction. Iterative optimization of SBI detection and stroke prediction (Stages 2–3) enhances model robustness. Adopted with permission from ref. 11 Copyright 2025, Springer Nature.
