- Diabetes PHGKB -
Last Posted: Nov 27, 2020
- Diving Deep into Deep Learning: An Update on Artificial Intelligence in Retina.
Goldhagen Brian E et al. Current ophthalmology reports 2020 Sep 8(3) 121-128
- Predicting the risk of developing diabetic retinopathy using deep learning
A Bora et al, Lancet Digital Health, November 26, 2020
- Five-Year Cost-Effectiveness Modeling of Primary Care-Based, Nonmydriatic Automated Retinal Image Analysis Screening Among Low-Income Patients with Diabetes.
Fuller Spencer D et al. Journal of diabetes science and technology 2020 Oct 1932296820967011
- Artificial intelligence-enabled screening for diabetic retinopathy: a real-world, multicenter and prospective study.
Zhang Yifei et al. BMJ open diabetes research & care 2020 Oct 8(1)
- The association of Interieukin-6 polymorphism (rs1800795) with microvascular complications in type 2 diabetes mellitus.
Cui Jieyuan et al. Bioscience reports 2020 Oct
- Real-World Clinical Experience With Idebenone in the Treatment of Leber Hereditary Optic Neuropathy.
Catarino Claudia B et al. Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society 2020 Sep
- Automated diabetic retinopathy detection with two different retinal imaging devices using artificial intelligence: a comparison study.
Sarao Valentina et al. Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie 2020 Sep
- Evidence Based Prediction and Progression Monitoring on Retinal Images from Three Nations.
Al Turk Lutfiah et al. Translational vision science & technology 2020 Aug 9(2) 44
- Leveraging Multimodal Deep Learning Architecture with Retina Lesion Information to Detect Diabetic Retinopathy.
Tseng Vincent S et al. Translational vision science & technology 2020 Jul 9(2) 41
- THEIA™ development, and testing of artificial intelligence-based primary triage of diabetic retinopathy screening images in New Zealand.
Vaghefi E et al. Diabetic medicine : a journal of the British Diabetic Association 2020 Aug
- Machine Learning Techniques for Ophthalmic Data Processing: A Review.
Sarhan Mhd Hasan et al. IEEE journal of biomedical and health informatics 2020 Jul PP
- Simultaneous Diagnosis of Severity and Features of Diabetic Retinopathy in Fundus Photography Using Deep Learning.
Wang Juan et al. IEEE journal of biomedical and health informatics 2020 Jul PP
- The Evolution of Diabetic Retinopathy Screening Programmes: A Chronology of Retinal Photography from 35 mm Slides to Artificial Intelligence.
Huemer Josef et al. Clinical ophthalmology (Auckland, N.Z.) 2020 142021-2035
- Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach.
Bello-Chavolla Omar Yaxmehen et al. BMJ open diabetes research & care 2020 Jul 8(1)
- Economic Challenges of Artificial Intelligence Adoption for Diabetic Retinopathy.
Chen Evan M et al. Ophthalmology 2020 Jul
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