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Automated detection of Diabetic Retinopathy in Three European Populations

Hansen, MB, Tang, Hongying, Wang, Su, Al Turk, L, Piermarocchi, R, Speckauskas, M, Hense, H-W, Leung, I and Peto, T (2016) Automated detection of Diabetic Retinopathy in Three European Populations Journal of Clinical & Experimental Ophthalmology, 7 (4), 1000582.

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Objective: Currently 1/12 of the world’s population has diabetes mellitus (DM), many are or will be screened by having retinal images taken. This current study aims to compare the DAPHNE software’s ability to detect DR in three different European populations compared to human grading carried out at the Moorfields Eye Hospital Reading Centre (MEHRC). Participants: Retinal images were taken from participants of the HAPIEE study (Lithuania, n=1014), the PAMDI study (Italy, n=882) and the MARS study (Germany, n=909). Methods: All anonymized images were graded by human graders at MEHRC for the presence of DR. Independently, and without any knowledge of the human grader’s results, the DAPHNE software analysed the images and divided the participants into DR and no-DR groups. Main outcome measures: The primary outcomes were sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the DAPHNE software with regards to the identification of DR or no-DR on retinal images as compared to the human grader as reference standard. Results: A total of 2805 participants were enrolled from the three study sites. The sensitivity of the DAPHNE software was above 93% in all three studies specificity was above 80%, the PPV was above 28% and the NPV was not below 98.8% in any of the studies. The DAPHNE software did not miss any vision-threatening DR. The areas under the curve (AUC) for all three studies were above 0.96. DAPHNE reduced manual human workload by 70% but had a total false positive rate of 63%. Conclusions: The DAPHNE software showed to be reliable to detect DR on three different European populations, using three different imaging settings. Further testing is required to see scalability, performance on live DR screening systems and on camera settings different to these studies.

Item Type: Article
Subjects : Computer Science
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Date : 4 August 2016
Identification Number : 10.4172/2155-9570.1000582
Copyright Disclaimer : Copyright: © 2016 Hansen MB, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Uncontrolled Keywords : Diabetic retinopathy; Automated grading; Europe; Diabetic retinopathy screening; Diabetes
Depositing User : Symplectic Elements
Date Deposited : 21 Sep 2016 10:04
Last Modified : 08 Aug 2017 08:23

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