What's New

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