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Journal of Diabetes Research
Volume 2015, Article ID 623619, 8 pages
Research Article

Combined Methods for Diabetic Retinopathy Screening, Using Retina Photographs and Tear Fluid Proteomics Biomarkers

1Department of Computer Graphics and Image Processing, Bioinformatics Research Group, Faculty of Informatics, University of Debrecen, Egyetem tér 1, Debrecen 4032, Hungary
2Department of Ophthalmology, Faculty of Medicine, University of Debrecen, Egyetem tér 1, Debrecen 4032, Hungary
3Astridbio Technologies Inc., 439 University Avenue, Toronto, ON, Canada M5G 1Y8
4NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, 162 City Road, London EC1V 2PD, UK
5Department of Biochemistry and Molecular Biology, Proteomics Core Facility, Faculty of Medicine, University of Debrecen, Egyetem tér 1, Debrecen 4032, Hungary
6Centre for Research on Inner City Health, Keenan Research Centre, Li Ka Shing Knowledge Institute, St Michael’s Hospital, 30 Bond Street, Toronto, ON, Canada M5B 1W8
7InnoTears Ltd., Szent Anna Utca 37/1. 2. em. 1, Debrecen 4024, Hungary

Received 20 June 2014; Revised 26 September 2014; Accepted 29 September 2014

Academic Editor: Konstantinos Papatheodorou

Copyright © 2015 Zsolt Torok et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. It is estimated that 347 million people suffer from diabetes mellitus (DM), and almost 5 million are blind due to diabetic retinopathy (DR). The progression of DR can be slowed down with early diagnosis and treatment. Therefore our aim was to develop a novel automated method for DR screening. Methods. 52 patients with diabetes mellitus were enrolled into the project. Of all patients, 39 had signs of DR. Digital retina images and tear fluid samples were taken from each eye. The results from the tear fluid proteomics analysis and from digital microaneurysm (MA) detection on fundus images were used as the input of a machine learning system. Results. MA detection method alone resulted in 0.84 sensitivity and 0.81 specificity. Using the proteomics data for analysis 0.87 sensitivity and 0.68 specificity values were achieved. The combined data analysis integrated the features of the proteomics data along with the number of detected MAs in the associated image and achieved sensitivity/specificity values of 0.93/0.78. Conclusions. As the two different types of data represent independent and complementary information on the outcome, the combined model resulted in a reliable screening method that is comparable to the requirements of DR screening programs applied in clinical routine.