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

Al Turk, L; Wang, S; Krause, P; Wawrzynski, J; Saleh, GM; Alsawadi, H; Alshamrani, AZ; ... Tang, HL; + view all (2020) Evidence Based Prediction and Progression Monitoring on Retinal Images from Three Nations. Translational Vision Science & Technology , 9 (2) , Article 44. 10.1167/tvst.9.2.44. Green open access

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Abstract

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.

Type: Article
Title: Evidence Based Prediction and Progression Monitoring on Retinal Images from Three Nations
Open access status: An open access version is available from UCL Discovery
DOI: 10.1167/tvst.9.2.44
Publisher version: https://doi.org/10.1167/tvst.9.2.44
Language: English
Additional information: This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: diabetic retinopathy; lesion detection; deep learning; AI algorithm; diabetes
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Developmental Biology and Cancer Dept
URI: https://discovery.ucl.ac.uk/id/eprint/10119829
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