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Evidence Based Prediction and Progression Monitoring on Retinal Images from Three Nations

Al Turk, Lutfiah, Wang, Su, Krause, Paul, Wawrzynski, James, Saleh, George M., Alsawadi, Hend, Alshamrani, Abdulrahman Zaid, Peto, Tunde, Bastawrous, Andrew, Li, Jingren and Tang, Hongying Lilian (2020) Evidence Based Prediction and Progression Monitoring on Retinal Images from Three Nations Translational Vision Science & Technology, 9 (2), 44.

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Purpose: The aim of this work is to demonstrate how a retinal image analysis system, DAPHNE, supports the optimization of diabetic retinopathy (DR) screening programs for grading color fundus photography. Method: Retinal image sets, graded by trained and certified human graders, were acquired from Saudi Arabia, China, and Kenya. Each image was subsequently analyzed by the DAPHNE automated software. The sensitivity, specificity, and positive and negative predictive values for the detection of referable DR or diabetic macular edema were evaluated, taking human grading or clinical assessment outcomes to be the gold standard. The automated software’s ability to identify co-pathology and to correctly label DR lesions was also assessed. Results: In all three datasets the agreement between the automated software and human grading was between 0.84 to 0.88. Sensitivity did not vary significantly between populations (94.28%–97.1%) with specificity ranging between 90.33% to 92.12%. There were excellent negative predictive values above 93% in all image sets. The software was able to monitor DR progression between baseline and follow-up images with the changes visualized. No cases of proliferative DR or DME were missed in the referable recommendations. Conclusions: The DAPHNE automated software demonstrated its ability not only to grade images but also to reliably monitor and visualize progression. Therefore it has the potential to assist timely image analysis in patients with diabetes in varied populations and also help to discover subtle signs of sight-threatening disease onset. Translational Relevance: This article takes research on machine vision and evaluates its readiness for clinical use.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors :
Al Turk, Lutfiah
Wawrzynski, James
Saleh, George M.
Alsawadi, Hend
Alshamrani, Abdulrahman Zaid
Peto, Tunde
Bastawrous, Andrew
Li, Jingren
Tang, Hongying Lilian
Date : 7 August 2020
Funders : Engineering and Physical Sciences Research Council (EPSRC), National Institute for Health Research (NIHR), Biomedical Research Centre - Moorfields Eye Hospital, NHS Foundation Trust, UCL Institute of Ophthalmology, NSTIP strategic technologies program in the Kingdom of Saudi Arabia
DOI : 10.1167/tvst.9.2.44
Copyright Disclaimer : Copyright 2020 The Authors This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Projects : NSTIP strategic technologies program in the Kingdom of Saudi Arabia - 10-INF1262-03
Uncontrolled Keywords : diabetic retinopathy; lesion detection; deep learning; AI algorithm; diabetes
Additional Information : Embargo OK Metadata OK No Further Action
Depositing User : James Marshall
Date Deposited : 13 Aug 2020 12:42
Last Modified : 13 Aug 2020 12:42

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