ArticleNature biomedical engineering2022
Detection of signs of disease in external photographs of the eyes via deep learning.
Article in Nature biomedical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed, 93 citations in OpenAlex.
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- Which features best differentiate Sjögren's-related from non-Sjögren's-related dry eye disease?Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Swin-DRNet: A robust transformer framework for diabetic retinopathy screening under heterogeneous imaging conditions.Scientific reports · 2026Article
- Simulated Workflow Feasibility Evaluation of a Web-Based Periorbital Measurement Platform: Development and Usability Study.JMIR human factors · 2026Article
- Oculomics: advances and perspectives from traditional Chinese medicine to modern multimodal biomarkers.International journal of ophthalmology · 2026Review
- A deep-learning system for the assessment of coronary heart disease risk via scleral photographs.Scientific reports · 2025Article
- Article
- Effectiveness and satisfaction of fully self-service fundus disease screening among middle-aged individuals: a cross-sectional study.BMJ open ophthalmology · 2025Article
- Diagnosing pathologic myopia by identifying morphologic patterns using ultra widefield images with deep learning.NPJ digital medicine · 2025Article
- Ensemble fuzzy deep learning for brain tumor detection.Scientific reports · 2025Article
- Artificial intelligence in chronic kidney diseases: methodology and potential applications.International urology and nephrology · 2025Review
- Advances and prospects of multi-modal ophthalmic artificial intelligence based on deep learning: a review.Eye and vision (London, England) · 2024Review
- Retinal Imaging-Based Oculomics: Artificial Intelligence as a Tool in the Diagnosis of Cardiovascular and Metabolic Diseases.Biomedicines · 2024Review
- Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs.Eye (London, England) · 2024Article
- Deep learning for predicting circular retinal nerve fiber layer thickness from fundus photographs and diagnosing glaucoma.Heliyon · 2024Article
- Quantifying impairment and disease severity using AI models trained on healthy subjects.NPJ digital medicine · 2024Article
- Resilience to diabetic retinopathy.Progress in retinal and eye research · 2024Review
- Novel automated non-invasive detection of ocular surface squamous neoplasia using artificial intelligence.World journal of methodology · 2024Review
- Oculomics: A Crusade Against the Four Horsemen of Chronic Disease.Ophthalmology and therapy · 2024Review
- AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook.Eye (London, England) · 2024Review
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Retinal fundus photographs can be used to detect a range of retinal conditions. Here we show that deep-learning models trained instead on external photographs of the eyes can be used to detect diabetic retinopathy (DR), diabetic macular oedema and poor blood glucose control. We developed the models using eye photographs from 145,832 patients with diabetes from 301 DR screening sites and evaluated the models on four tasks and four validation datasets with a total of 48,644 patients from 198 additional screening sites. For all four tasks, the predictive performance of the deep-learning models was significantly higher than the performance of logistic regression models using self-reported demographic and medical history data, and the predictions generalized to patients with dilated pupils, to patients from a different DR screening programme and to a general eye care programme that included diabetics and non-diabetics. We also explored the use of the deep-learning models for the detection of elevated lipid levels. The utility of external eye photographs for the diagnosis and management of diseases should be further validated with images from different cameras and patient populations.
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