Crowdsourcing to Evaluate Fundus Photographs for the Presence of Glaucoma

Xueyang Wang; Lucy I Mudie; Mani Baskaran; Ching-Yu Cheng; Wallace L Alward; David S Friedman; Christopher J Brady

doi:10.1097/IJG.0000000000000660

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Crowdsourcing to Evaluate Fundus Photographs for the Presence of Glaucoma

Journal article

Peer reviewed

Crowdsourcing to Evaluate Fundus Photographs for the Presence of Glaucoma

Xueyang Wang, Lucy I Mudie, Mani Baskaran, Ching-Yu Cheng, Wallace L Alward, David S Friedman and Christopher J Brady

Journal of glaucoma, Vol.26(6), pp.505-510

06/2017

DOI: 10.1097/IJG.0000000000000660

PMCID: PMC5453824

PMID: 28319525

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https://www.ncbi.nlm.nih.gov/pmc/articles/5453824View

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Abstract

To assess the accuracy of crowdsourcing for grading optic nerve images for glaucoma using Amazon Mechanical Turk before and after training modules. Images (n=60) from 2 large population studies were graded for glaucoma status and vertical cup-to-disc ratio (VCDR). In the baseline trial, users on Amazon Mechanical Turk (Turkers) graded fundus photos for glaucoma and VCDR after reviewing annotated example images. In 2 additional trials, Turkers viewed a 26-slide PowerPoint training or a 10-minute video training and passed a quiz before being permitted to grade the same 60 images. Each image was graded by 10 unique Turkers in all trials. The mode of Turker grades for each image was compared with an adjudicated expert grade to determine accuracy as well as the sensitivity and specificity of Turker grading. In the baseline study, 50% of the images were graded correctly for glaucoma status and the area under the receiver operating characteristic (AUROC) was 0.75 [95% confidence interval (CI), 0.64-0.87]. Post-PowerPoint training, 66.7% of the images were graded correctly with AUROC of 0.86 (95% CI, 0.78-0.95). Finally, Turker grading accuracy was 63.3% with AUROC of 0.89 (95% CI, 0.83-0.96) after video training. Overall, Turker VCDR grades for each image correlated with expert VCDR grades (Bland-Altman plot mean difference=-0.02). Turkers graded 60 fundus images quickly and at low cost, with grading accuracy, sensitivity, and specificity, all improving with brief training. With effective education, crowdsourcing may be an efficient tool to aid in the identification of glaucomatous changes in retinal images.

Humans

Middle Aged

Optic Nerve Diseases - diagnosis

Sensitivity and Specificity

Telemedicine - methods

Female

Male

ROC Curve

Crowdsourcing - methods

Optic Disk - diagnostic imaging

Glaucoma - diagnosis

Photography - methods

Details

Title: Subtitle: Crowdsourcing to Evaluate Fundus Photographs for the Presence of Glaucoma
Creators: Xueyang Wang - Johns Hopkins School of Medicine, Wilmer Eye Institute, Baltimore, MD ∥Department of Ophthalmology and Visual Sciences, University of Iowa Carver College of Medicine, Iowa City, IA †Singapore National Eye Center, Singapore Eye Research Institute ‡Duke-NUS Medical School §Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
Lucy I Mudie
Mani Baskaran
Ching-Yu Cheng
Wallace L Alward
David S Friedman
Christopher J Brady
Resource Type: Journal article
Publication Details: Journal of glaucoma, Vol.26(6), pp.505-510
DOI: 10.1097/IJG.0000000000000660
PMID: 28319525
PMCID: PMC5453824
NLM abbreviation: J Glaucoma
ISSN: 1057-0829
eISSN: 1536-481X
Grant note: KL2 TR001077 / NCATS NIH HHS
Language: English
Date published: 06/2017
Academic Unit: Ophthalmology and Visual Sciences
Record Identifier: 9984101604502771

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