Automated Diabetic Retinopathy Image Assessment Software: Diagnostic Accuracy and Cost-Effectiveness Compared with Human Graders
Adnan Tufail, Caroline Rudisill, Catherine Egan, Venediktos V Kapetanakis, Sebastian Salas-Vega, Christopher G Owen, Aaron Lee, Vern Louw, John Anderson, Gerald Liew, Louis Bolter, Sowmya Srinivas, Muneeswar Nittala, SriniVas Sadda, Paul Taylor, Alicja R Rudnicka, Adnan Tufail, Caroline Rudisill, Catherine Egan, Venediktos V Kapetanakis, Sebastian Salas-Vega, Christopher G Owen, Aaron Lee, Vern Louw, John Anderson, Gerald Liew, Louis Bolter, Sowmya Srinivas, Muneeswar Nittala, SriniVas Sadda, Paul Taylor, Alicja R Rudnicka
Abstract
Objective: With the increasing prevalence of diabetes, annual screening for diabetic retinopathy (DR) by expert human grading of retinal images is challenging. Automated DR image assessment systems (ARIAS) may provide clinically effective and cost-effective detection of retinopathy. We aimed to determine whether ARIAS can be safely introduced into DR screening pathways to replace human graders.
Design: Observational measurement comparison study of human graders following a national screening program for DR versus ARIAS.
Participants: Retinal images from 20 258 consecutive patients attending routine annual diabetic eye screening between June 1, 2012, and November 4, 2013.
Methods: Retinal images were manually graded following a standard national protocol for DR screening and were processed by 3 ARIAS: iGradingM, Retmarker, and EyeArt. Discrepancies between manual grades and ARIAS results were sent to a reading center for arbitration.
Main outcome measures: Screening performance (sensitivity, false-positive rate) and diagnostic accuracy (95% confidence intervals of screening-performance measures) were determined. Economic analysis estimated the cost per appropriate screening outcome.
Results: Sensitivity point estimates (95% confidence intervals) of the ARIAS were as follows: EyeArt 94.7% (94.2%-95.2%) for any retinopathy, 93.8% (92.9%-94.6%) for referable retinopathy (human graded as either ungradable, maculopathy, preproliferative, or proliferative), 99.6% (97.0%-99.9%) for proliferative retinopathy; Retmarker 73.0% (72.0 %-74.0%) for any retinopathy, 85.0% (83.6%-86.2%) for referable retinopathy, 97.9% (94.9%-99.1%) for proliferative retinopathy. iGradingM classified all images as either having disease or being ungradable. EyeArt and Retmarker saved costs compared with manual grading both as a replacement for initial human grading and as a filter prior to primary human grading, although the latter approach was less cost-effective.
Conclusions: Retmarker and EyeArt systems achieved acceptable sensitivity for referable retinopathy when compared with that of human graders and had sufficient specificity to make them cost-effective alternatives to manual grading alone. ARIAS have the potential to reduce costs in developed-world health care economies and to aid delivery of DR screening in developing or remote health care settings.
Copyright © 2016 American Academy of Ophthalmology. All rights reserved.
Source: PubMed