作者
Rory Sayres, Ankur Taly, Ehsan Rahimy, Katy Blumer, David Coz, Naama Hammel, Jonathan Krause, Arunachalam Narayanaswamy, Zahra Rastegar, Derek Wu, Shawn Xu, Scott Barb, Anthony Joseph, Michael Shumski, Jesse Smith, Arjun B Sood, Greg S Corrado, Lily Peng, Dale R Webster
发表日期
2019/4/1
期刊
Ophthalmology
卷号
126
期号
4
页码范围
552-564
出版商
Elsevier
简介
Purpose
To understand the impact of deep learning diabetic retinopathy (DR) algorithms on physician readers in computer-assisted settings.
Design
Evaluation of diagnostic technology.
Participants
One thousand seven hundred ninety-six retinal fundus images from 1612 diabetic patients.
Methods
Ten ophthalmologists (5 general ophthalmologists, 4 retina specialists, 1 retina fellow) read images for DR severity based on the International Clinical Diabetic Retinopathy disease severity scale in each of 3 conditions: unassisted, grades only, or grades plus heatmap. Grades-only assistance comprised a histogram of DR predictions (grades) from a trained deep-learning model. For grades plus heatmap, we additionally showed explanatory heatmaps.
Main Outcome Measures
For each experiment arm, we computed sensitivity and specificity of each reader and the algorithm for different levels of DR severity against an …
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R Sayres, A Taly, E Rahimy, K Blumer, D Coz… - Ophthalmology, 2019