ArticleNPJ digital medicine2019
Deep learning in estimating prevalence and systemic risk factors for diabetic retinopathy: a multi-ethnic study.
Article in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it, 83 citations in OpenAlex.
- Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application.Current diabetes reports · 2019Pooled it
- Artificial intelligence in diabetic retinopathy: from automated screening to risk-stratified care.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Enhancing AI-driven forecasting of diabetes burden: a comparative analysis of deep learning and statistical models.Scientific reports · 2025Article
- Accelerated Biological Aging in Exfoliation Glaucoma Assessed by Fundus-Derived Predicted Age and Advanced Glycation End Products.International journal of molecular sciences · 2025Article
- Fundus-Derived Predicted Age Acceleration in Glaucoma Patients Using Deep Learning and Propensity Score-Matched Controls.Journal of clinical medicine · 2025Article
- The novel antidiabetic medications on diabetic retinopathy: relevant molecular mechanisms, advancing diagnostic innovations, and therapeutic implications.Frontiers in medicine · 2025Review
- DDLA: a double deep latent autoencoder for diabetic retinopathy diagnose based on continuous glucose sensors.Medical & biological engineering & computing · 2024Article
- Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Article
- Identifying Diabetic Retinopathy in the Human Eye: A Hybrid Approach Based on a Computer-Aided Diagnosis System Combined with Deep Learning.Tomography (Ann Arbor, Mich.) · 2024Article
- Evaluation and Optimization of Diabetic Retinopathy Screenings for Uninsured Latinx Patients in a Resource-Limited Student-Run Free Clinic.Journal of student-run clinics · 2024Article
- Association between blood urea nitrogen levels and diabetic retinopathy in diabetic adults in the United States (NHANES 2005-2018).Frontiers in endocrinology · 2024Article
- The Diabetic Retinopathy "Pandemic" and Evolving Global Strategies: The 2023 Friedenwald Lecture.Investigative ophthalmology & visual science · 2023Article
- Artificial intelligence for diabetic retinopathy in low-income and middle-income countries: a scoping review.BMJ open diabetes research & care · 2023Article
- What, why and how to monitor blood glucose in critically ill patients.World journal of diabetes · 2023Review
- Low serum dehydroepiandrosterone levels are associated with diabetic retinopathy in patients with type 2 diabetes mellitus.Journal of diabetes investigation · 2023Article
- Diagnostic test accuracy of artificial intelligence in screening for referable diabetic retinopathy in real-world settings: A systematic review and meta-analysis.PLOS global public health · 2023Article
- Prediction of Visual Outcomes After Diabetic Vitrectomy Using Clinical Factors From Common Data Warehouse.Translational vision science & technology · 2022Article
- Long-term prediction models for vision-threatening diabetic retinopathy using medical features from data warehouse.Scientific reports · 2022Article
- Application value of a deep learning method based on a 3D V-Net convolutional neural network in the recognition and segmentation of the auditory ossicles.Frontiers in neuroinformatics · 2022Article
- A Deep Learning-Based Framework for Accurate Evaluation of Corneal Treatment Zone After Orthokeratology.Translational vision science & technology · 2021Article
Corrections and comments
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Authors and funding
17 authors at 6 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In any community, the key to understanding the burden of a specific condition is to conduct an epidemiological study. The deep learning system (DLS) recently showed promising diagnostic performance for diabetic retinopathy (DR). This study aims to use DLS as the grading tool, instead of human assessors, to determine the prevalence and the systemic cardiovascular risk factors for DR on fundus photographs, in patients with diabetes. This is a multi-ethnic (5 races), multi-site (8 datasets from Singapore, USA, Hong Kong, China and Australia), cross-sectional study involving 18,912 patients (
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