From retina to brain: how deep learning closes the gap in silent stroke screening
Silent brain infarctions (SBIs), affecting 20% of adults and increasing stroke risk, evade routine MRI screening. While retinal scans offer a “window to the brain,” prior AI failed to simultaneously detect SBIs and predict strokes. DeepRETStroke overcomes this by analysing eye scans. Trained on ~900,000 images, it uses deep learning combining self-supervised pattern recognition from unlabeled images, semi-supervised SBI detection with limited MRI, and knowledge transfer refinement, transforming eye exams into affordable stroke screenings.
Authors
- Y S Wang (ORCID: https://orcid.org/0009-0004-1647-5761)
- Shumao Xu
- Mingyuan Ge (ORCID: https://orcid.org/0000-0001-5682-7443)
Institutions
- Fudan University (CN)
- Shanghai Institute for Science of Science (CN)
- Huashan Hospital (CN)
- Shanghai Center for Brain Science and Brain-Inspired Technology (CN)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2025-11-13
- DOI
- https://doi.org/10.1038/s41746-025-02021-2
- Citations
- 1
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- National Natural Science Foundation of China
- Fudan University