ArticleThe Lancet. Digital health2023
A deep learning model for novel systemic biomarkers in photographs of the external eye: a retrospective study.
Article in The Lancet. Digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transformative applications of oculomics-based AI approaches in the management of systemic diseases: A systematic review.Journal of advanced research · 2025Pooled it
- Applications of Machine Learning in Noninvasive Anemia Diagnosis: A Systematic Review Based on Cross-Industry Standard Process for Data Mining.Health science reports · 2026Review
- Simulated Workflow Feasibility Evaluation of a Web-Based Periorbital Measurement Platform: Development and Usability Study.JMIR human factors · 2026Article
- Evolution of Oculomics: From Naked Eye Observations to Artificial Intelligence over 100 Years.Ophthalmology science · 2026Article
- Routine blood tests and machine learning identify complications in high myopia.Nature communications · 2026Article
- Oculomics: advances and perspectives from traditional Chinese medicine to modern multimodal biomarkers.International journal of ophthalmology · 2026Review
- Article
- Role of artificial intelligence-based ocular biomarkers in hepatobiliary diseases: A scoping review.World journal of hepatology · 2025Review
- Phenotypic screening and genetic insights for predicting major vascular-related diseases using retinal imaging.NPJ digital medicine · 2025Article
- Cornea Oculomics: A Clinical Blueprint for Extending Corneal Diagnostics and Artificial Intelligence in Systemic Health Insights.Diagnostics (Basel, Switzerland) · 2025Review
- Application of ChatGPT-4 to oculomics: a cost-effective osteoporosis risk assessment to enhance management as a proof-of-principles model in 3PM.The EPMA journal · 2024Article
- Diffuse reflectance spectroscopy and RGB-imaging: a comparative study of non-invasive haemoglobin assessment.Scientific reports · 2024Article
- Dataset of human skin and fingernails images for non-invasive haemoglobin level assessment.Scientific data · 2024Article
- Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs.Eye (London, England) · 2024Article
- Diabetes Technology Meeting 2023.Journal of diabetes science and technology · 2024Article
- Machine-learning-based models to predict cardiovascular risk using oculomics and clinic variables in KNHANES.BioData mining · 2024Article
- AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook.Eye (London, England) · 2024Review
- The dawn of multimodal artificial intelligence in nephrology.Nature reviews. Nephrology · 2024Article
- Development of oculomics artificial intelligence for cardiovascular risk factors: A case study in fundus oculomics for HbA1c assessment and clinically relevant considerations for clinicians.Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)Article
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Abstract
backgroundPhotographs of the external eye were recently shown to reveal signs of diabetic retinal disease and elevated glycated haemoglobin. This study aimed to test the hypothesis that external eye photographs contain information about additional systemic medical conditions.
methodsWe developed a deep learning system (DLS) that takes external eye photographs as input and predicts systemic parameters, such as those related to the liver (albumin, aspartate aminotransferase [AST]); kidney (estimated glomerular filtration rate [eGFR], urine albumin-to-creatinine ratio [ACR]); bone or mineral (calcium); thyroid (thyroid stimulating hormone); and blood (haemoglobin, white blood cells [WBC], platelets). This DLS was trained using 123 130 images from 38 398 patients with diabetes undergoing diabetic eye screening in 11 sites across Los Angeles county, CA, USA. Evaluation focused on nine prespecified systemic parameters and leveraged three validation sets (A, B, C) spanning 25 510 patients with and without diabetes undergoing eye screening in three independent sites in Los Angeles county, CA, and the greater Atlanta area, GA, USA. We compared performance against baseline models incorporating available clinicodemographic variables (eg, age, sex, race and ethnicity, years with diabetes).
findingsRelative to the baseline, the DLS achieved statistically significant superior performance at detecting AST >36·0 U/L, calcium <8·6 mg/dL, eGFR <60·0 mL/min/1·73 m
interpretationWe found further evidence that external eye photographs contain biomarkers spanning multiple organ systems. Such biomarkers could enable accessible and non-invasive screening of disease. Further work is needed to understand the translational implications.
fundingGoogle.
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