作者
Sonia Phene, R Carter Dunn, Naama Hammel, Yun Liu, Jonathan Krause, Naho Kitade, Mike Schaekermann, Rory Sayres, Derek J Wu, Ashish Bora, Christopher Semturs, Anita Misra, Abigail E Huang, Arielle Spitze, Felipe A Medeiros, April Y Maa, Monica Gandhi, Greg S Corrado, Lily Peng, Dale R Webster
发表日期
2019/12/1
期刊
Ophthalmology
卷号
126
期号
12
页码范围
1627-1639
出版商
Elsevier
简介
Purpose
To develop and validate a deep learning (DL) algorithm that predicts referable glaucomatous optic neuropathy (GON) and optic nerve head (ONH) features from color fundus images, to determine the relative importance of these features in referral decisions by glaucoma specialists (GSs) and the algorithm, and to compare the performance of the algorithm with eye care providers.
Design
Development and validation of an algorithm.
Participants
Fundus images from screening programs, studies, and a glaucoma clinic.
Methods
A DL algorithm was trained using a retrospective dataset of 86 618 images, assessed for glaucomatous ONH features and referable GON (defined as ONH appearance worrisome enough to justify referral for comprehensive examination) by 43 graders. The algorithm was validated using 3 datasets: dataset A (1205 images, 1 image/patient; 18.1% referable), images adjudicated by panels …
学术搜索中的文章
S Phene, RC Dunn, N Hammel, Y Liu, J Krause… - Ophthalmology, 2019