Accuracy of Artificial Intelligence in Estimating Best-Corrected Visual Acuity From Fundus Photographs in Eyes With Diabetic Macular Edema
Authors:
Open Access
- 1 July 2023
- journal article
- research article
- Published by American Medical Association (AMA) in JAMA Ophthalmology
- Vol. 141 (7) , 677-685
Abstract
In 2014, approximately 19.8 million individuals in the United States 40 years or older had diabetes.1 Among these individuals, about 14.3 million had diabetes without diabetic retinopathy (including absence of diabetic macular edema [DME]), 4.7 million had diabetic retinopathy without DME, and approximately 746 000 had DME.1 Extrapolating these numbers to the estimated 396 million individuals 40 years or older with diabetes worldwide in 20192 suggests that 381.8 million have diabetic retinopathy without DME and 14.2 million have DME.Keywords
This publication has 24 references indexed in Scilit:
- Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural NetworksJAMA Ophthalmology, 2017
- Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus PhotographsJAMA, 2016
- Intravitreal Aflibercept for Diabetic Macular EdemaOphthalmology, 2015
- Prevalence of and Risk Factors for Diabetic Macular Edema in the United StatesJAMA Ophthalmology, 2014
- Estimating physiological skin parameters from hyperspectral signaturesJournal of Biomedical Optics, 2013
- Ranibizumab for Diabetic Macular EdemaOphthalmology, 2012
- Rationale for the Diabetic Retinopathy Clinical Research Network Treatment Protocol for Center-Involved Diabetic Macular EdemaOphthalmology, 2011
- Automatic screening of age-related macular degeneration and retinal abnormalities2011
- A computerized method of visual acuity testingAmerican Journal of Ophthalmology, 2003
- Recurrent Neural Networks for Prediction2001