ArticleNutrition, metabolism, and cardiovascular diseases : NMCD2022
A deep learning model for screening type 2 diabetes from retinal photographs.
Article in Nutrition, metabolism, and cardiovascular diseases : NMCD, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.Frontiers in digital health · 2025Pooled it
- A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images.Translational vision science & technology · 2023Pooled it
- 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
- Artificial intelligence-enhanced electrocardiography for the prediction of future type 2 diabetes mellitus: a model-development and multicentre validation study.European heart journal. Digital health · 2026Article
- Tracer Techniques in Ophthalmology: Ocular Applications and Systemic Connections.Diagnostics (Basel, Switzerland) · 2026Review
- A digital twin framework for predicting and simulating type 2 diabetes onset using retrospective lifestyle data.Frontiers in digital health · 2026Article
- Statistical and machine-learning assessment of attitudinal, knowledge, and perceptual factors on diabetes awareness in Kuwait.BMC medical informatics and decision making · 2025Article
- Vision transformer-based stratification of pre/diabetic and pre/hypertensive patients from retinal photographs for 3PM applications.The EPMA journal · 2025Article
- Use of artificial intelligence with retinal imaging in screening for diabetes-associated complications: systematic review.EClinicalMedicine · 2025Review
- Harnessing gut-derived bioactives and AI diagnostics for the next generation of type 2 diabetes solutions.Frontiers in endocrinology · 2025Review
- Artificial intelligence-enhanced retinal imaging as a biomarker for systemic diseases.Theranostics · 2025Review
- Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.BMC public health · 2024Article
- DiaNet v2 deep learning based method for diabetes diagnosis using retinal images.Scientific reports · 2024Article
- A scoping review of artificial intelligence-based methods for diabetes risk prediction.NPJ digital medicine · 2023Article
- Deep Learning Algorithms for Screening and Diagnosis of Systemic Diseases Based on Ophthalmic Manifestations: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Deep learning-based fundus image analysis for cardiovascular disease: a review.Therapeutic advances in chronic disease · 2023Review
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9 authors.
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Abstract
BACKGROUND AND
aimsWe aimed to develop and evaluate a non-invasive deep learning algorithm for screening type 2 diabetes in UK Biobank participants using retinal images. METHODS AND
resultsThe deep learning model for prediction of type 2 diabetes was trained on retinal images from 50,077 UK Biobank participants and tested on 12,185 participants. We evaluated its performance in terms of predicting traditional risk factors (TRFs) and genetic risk for diabetes. Next, we compared the performance of three models in predicting type 2 diabetes using 1) an image-only deep learning algorithm, 2) TRFs, 3) the combination of the algorithm and TRFs. Assessing net reclassification improvement (NRI) allowed quantification of the improvement afforded by adding the algorithm to the TRF model. When predicting TRFs with the deep learning algorithm, the areas under the curve (AUCs) obtained with the validation set for age, sex, and HbA1c status were 0.931 (0.928-0.934), 0.933 (0.929-0.936), and 0.734 (0.715-0.752), respectively. When predicting type 2 diabetes, the AUC of the composite logistic model using non-invasive TRFs was 0.810 (0.790-0.830), and that for the deep learning model using only fundus images was 0.731 (0.707-0.756). Upon addition of TRFs to the deep learning algorithm, discriminative performance was improved to 0.844 (0.826-0.861). The addition of the algorithm to the TRFs model improved risk stratification with an overall NRI of 50.8%.
conclusionOur results demonstrate that this deep learning algorithm can be a useful tool for stratifying individuals at high risk of type 2 diabetes in the general population.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.