Diabetic Retinopathy
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Last Posted: May 21, 2024
- Machine Learning-Based Predictive Modeling of Diabetic Nephropathy in Type 2 Diabetes Using Integrated Biomarkers: A Single-Center Retrospective Study.
Ying Zhu et al. Diabetes Metab Syndr Obes 2024 171987-1997 - Comparison of 21 artificial intelligence algorithms in automated diabetic retinopathy screening using handheld fundus camera.
Anna-Maria Kubin et al. Ann Med 2024 56(1) 2352018 - Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases.
Uday Pratap Singh Parmar et al. Medicina (Kaunas) 2024 60(4) - Autonomous artificial intelligence versus teleophthalmology for diabetic retinopathy.
Donatella Musetti et al. Eur J Ophthalmol 2024 11206721241248856 - Development and External Validation of Machine Learning Models for Diabetic Microvascular Complications: Cross-Sectional Study With Metabolites.
Feng He et al. J Med Internet Res 2024 26e41065 - Patients Perceptions of Artificial Intelligence in a Deep Learning-Assisted Diabetic Retinopathy Screening Event: A Real-World Assessment.
Fernando Korn Malerbi et al. J Diabetes Sci Technol 2024 19322968241234378 - Automated Machine Learning for Predicting Diabetic Retinopathy Progression From Ultra-Widefield Retinal Images.
Paolo S Silva et al. JAMA Ophthalmol 2024 - Recommendations for initial diabetic retinopathy screening of diabetic patients using large language model-based artificial intelligence in real-life case scenarios.
Nikhil Gopalakrishnan et al. Int J Retina Vitreous 2024 10(1) 11 - Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial.
Risa M Wolf et al. Nat Commun 2024 15(1) 421 - Machine Learning Models for Prediction of Diabetic Microvascular Complications.
Sarah Kanbour et al. J Diabetes Sci Technol 2024 19322968231223726 - Real-world evaluation of smartphone-based artificial intelligence to screen for diabetic retinopathy in Dominica: a clinical validation study.
Oliver Kemp et al. BMJ Open Ophthalmol 2023 8(1) - Risk Stratification for Diabetic Retinopathy Screening Order Using Deep Learning: A Multicenter Prospective Study.
Ashish Bora et al. Transl Vis Sci Technol 2023 12(12) 11 - Clinician-Driven AI: Code-Free Self-Training on Public Data for Diabetic Retinopathy Referral.
Edward Korot et al. JAMA Ophthalmol 2023 - Artificial intelligence for telemedicine diabetic retinopathy screening: a review.
Luis Filipe Nakayama et al. Ann Med 2023 55(2) 2258149 - Deep learning detection of diabetic retinopathy in Scotland's diabetic eye screening programme.
Alan D Fleming et al. Br J Ophthalmol 2023 - Feasibility and accuracy of the screening for diabetic retinopathy using a fundus camera and an artificial intelligence pre-evaluation application.
A Piatti et al. Acta Diabetol 2023 - Prediction of diabetic kidney disease risk using machine learning models: a population-based cohort study of Asian adults.
Charumathi Sabanayagam et al. Elife 2023 12 - Smartphone Eye Examination: Artificial Intelligence and Telemedicine.
Manuel Augusto Pereira Vilela et al. Telemed J E Health 2023 - The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use.
Ji-Won Chun et al. J Korean Med Sci 2023 38(31) e253 - Efficacy of deep learning-based artificial intelligence models in screening and referring patients with diabetic retinopathy and glaucoma.
Janani Surya et al. Indian J Ophthalmol 2023 71(8) 3039-3045
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About Diabetes PHGKB
Diabetes PHGKB is an online, continuously updated, searchable database of published scientific literature, CDC and NIH resources, and other materials that address the translation of genomic and other precision health discoveries into improved health care and prevention related to diabetes...more
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Common Diabetes Related Topics
Disclaimer: Articles listed in the Public Health Knowledge Base are selected by Public Health Genomics Branch to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
- Page last reviewed:Feb 1, 2024
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