ArticleJournal of clinical medicine2022
Detection of Stroke with Retinal Microvascular Density and Self-Supervised Learning Using OCT-A and Fundus Imaging.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 25 citations in OpenAlex.
- The Underutilized Ocular Fundus in Emergency Departments: Current Progress and Future Prospect of Imaging and Artificial Intelligence for Patient Care.Ophthalmology science · 2026Review
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Multimodal imaging in neuro-ophthalmology.Indian journal of ophthalmology · 2026Review
- Using retinal diagnostics as a biomarker for neurodegenerative diseases: protocol for a systematic review.BMJ open · 2025Article
- Retinal Microvascular Characteristics-Novel Risk Stratification in Cardiovascular Diseases.Diagnostics (Basel, Switzerland) · 2025Article
- High-resolution fundus images for ophthalmomics and early cardiovascular disease prediction.Scientific data · 2025Article
- Artificial intelligence-enhanced retinal imaging as a biomarker for systemic diseases.Theranostics · 2025Review
- Artificial intelligence in stroke risk assessment and management via retinal imaging.Frontiers in computational neuroscience · 2025Review
- Study of Macular Microvascular Changes in Patients with Symptomatic Lacunar Cerebral Infarction Using Viaoptical Coherence Tomography Angiography (OCTA).International journal of general medicine · 2025Article
- Insight into the Brain: Application of the Retinal Microvasculature as a Biomarker for Cerebrovascular Diseases through Optical Coherence Tomography Angiography.Current ophthalmology reports · 2024Article
- The Role of Ophthalmology in Tele-Stroke Consults for Triaging Acute Vision Loss.Open access emergency medicine : OAEM · 2024Article
- Retinal Findings and Cardiovascular Risk: Prognostic Conditions, Novel Biomarkers, and Emerging Image Analysis Techniques.Journal of personalized medicine · 2023Review
- Space exploration as a catalyst for medical innovations.Frontiers in medicine · 2023Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
Acute cerebral stroke is a leading cause of disability and death, which could be reduced with a prompt diagnosis during patient transportation to the hospital. A portable retina imaging system could enable this by measuring vascular information and blood perfusion in the retina and, due to the homology between retinal and cerebral vessels, infer if a cerebral stroke is underway. However, the feasibility of this strategy, the imaging features, and retina imaging modalities to do this are not clear. In this work, we show initial evidence of the feasibility of this approach by training machine learning models using feature engineering and self-supervised learning retina features extracted from OCT-A and fundus images to classify controls and acute stroke patients. Models based on macular microvasculature density features achieved an area under the receiver operating characteristic curve (AUC) of 0.87-0.88. Self-supervised deep learning models were able to generate features resulting in AUCs ranging from 0.66 to 0.81. While further work is needed for the final proof for a diagnostic system, these results indicate that microvasculature density features from OCT-A images have the potential to be used to diagnose acute cerebral stroke from the retina.
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Registered trials
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