Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks

By Philippe M. Burlina, Neil J. Joshi, M. Pekala, Katia D. Pacheco, D. E. Freund, N. Bressler. JAMA ophthalmology, 2017

586 citations recorded by the source corpus.

Subfields: Epidemiology, Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c0fe78d0cee0e9a776695de12f020c21b01728ea

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ophthalmology

1. Daniel Shu Wei Ting
2. Tien Yin Wong
3. Pearse A. Keane

In Radiology, Nuclear Medicine and Imaging

1. Daniel Shu Wei Ting
2. Tien Yin Wong
3. Aaron Y. Lee
4. Pearse A. Keane
5. Danijel Skocaj

Citations in the corpus

• Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography (2020, background): “Recent studies demonstrated the potential of integrating AI into both the eye and childhood-disease diagnostic systems and found that it significantly improved clinical diagnostic efficiency and accuracy (Burlina et al., 2017; Kermany et al., 2018; Long et al., 2017; van Grinsven et al., 2016).”
• Artificial intelligence in retina. (2018, methodology): “Detection of referable AMD from CFP images was trained and evaluated on 130,000 stereo images from 4613 patients forming the AREDS data set (Burlina et al., 2017).”
• REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs (2019, unknown): “g the retina (Abramo et al., 2010; Schmidt-Erfurth et al., 2018). Its widespread availability makes it ideal for assessing several ophthalmic diseases such as age-related macular degeneration (AMD) (Burlina et al., 2017), diabetic retinopathy (DR) (Gulshan et al., 2016) and glaucoma (Li et al., 2018”
• Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective (2020, methodology): “…et al., 2019; Ting et al., 2017), glaucoma (Liu et al., 2019; Li et al., 2018; Masumoto et al., 2018a; Asaoka et al., 2016), age-related macular degeneration (AMD) (Grassmann et al., 2018; Burlina et al., 2017) and retinopathy of prematurity (ROP) (Brown et al., 2018) with high accuracy.”
• Artificial intelligence and deep learning in ophthalmology (2018, methodology): “Using a fivefold cross-validation, Burlina et al17 reported a diagnostic accuracy of between 88.4% and 91.6%, with an AUC of between 0.94 and 0.96.”