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

Journal of Diabetes Research

Volumn 2015, Issue , 2015, Pages

  Torok, Zsolt ^a,b,c   Peto, Tunde ^d   Csosz, Eva ^b   Tukacs, Edit ^a,c   Molnar, Agnes M ^e   Berta, Andras ^b,f   Tozser, Jozsef ^b,f   Hajdu, Andras ^a   Nagy, Valeria ^b   Domokos, Balint ^c   Csutak, Adrienne ^b,f  

^a UNIVERSITY OF DEBRECEN   (Hungary)

^b UNIVERSITY OF DEBRECEN   (Hungary)

^c Astridbio Technologies Inc   (Canada)

^d MOORFIELDS EYE HOSPITAL   (United Kingdom)

^e ST MICHAEL'S HOSPITAL   (Canada)

^f Inno Tears Ltd   (Hungary)

Author keywords

[No Author keywords available]

Indexed keywords

TEAR PROTEIN; BIOLOGICAL MARKER; PROTEOME;

AGED; ARTICLE; AUTOMATION; DIABETES MELLITUS; DIABETIC RETINOPATHY; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DISEASE DURATION; EYE PHOTOGRAPHY; FEMALE; HUMAN; IMAGE PROCESSING; LACRIMAL FLUID LEVEL; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MICROANEURYSM; MICROANEURYSM DETECTOR; OPHTHALMOLOGICAL DIAGNOSTIC DEVICE; PREDICTIVE VALUE; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEOMICS; RETINA IMAGE; SCREENING TEST; SENSITIVITY AND SPECIFICITY; VITREOUS BODY; ANEURYSM; CASE CONTROL STUDY; EYE FUNDUS; LACRIMAL FLUID; MASS SCREENING; METABOLISM; MIDDLE AGED; PHOTOGRAPHY; RETINA; RETINA BLOOD VESSEL;

AGED; ANEURYSM; BIOMARKERS; CASE-CONTROL STUDIES; DIABETES MELLITUS; DIABETIC RETINOPATHY; FEMALE; FUNDUS OCULI; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MACHINE LEARNING; MALE; MASS SCREENING; MIDDLE AGED; PHOTOGRAPHY; PROTEOME; PROTEOMICS; RETINA; RETINAL VESSELS; SENSITIVITY AND SPECIFICITY; TEARS;

EID: 84937012921     PISSN: 23146745     EISSN: 23146753     Source Type: Journal    
DOI: 10.1155/2015/623619     Document Type: Article

References (40)

1. G. Danaei, M.M. Finucane, Y. Lu et al., "National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million participants," The Lancet, vol. 378, no. 9785, pp. 31-40, 2011.
2. D. A. Antonetti, R. Klein, and T. W. Gardner, "Diabetic retinopathy," The New England Journal of Medicine, vol. 366, no. 13, pp. 1227-1239, 2012.
3. American Academy of Ophthalmology, Preferred Practice Pattern Guidelines: Diabetic Retinopathy, American Academy of Ophthalmology, 2008.
4. S. Wild, G. Roglic, A. Green, R. Sicree, and H. King, "Global prevalence of diabetes: estimates for the year 2000 and projections for 2030," Diabetes Care, vol. 27, no. 5, pp. 1047-1053, 2004.
5. N. Cheung, P. Mitchell, and T. Y. Wong, "Diabetic retinopathy," The Lancet, vol. 376, no. 9735, pp. 124-136, 2010.
6. J. P. O'Hare, A. Hopper, C. Madhaven et al., "Adding retinal photography to screening for diabetic retinopathy: a prospective study in primary care," The British Medical Journal, vol. 312, no. 7032, pp. 679-682, 1996.
7. Screening for Diabetic Retinopathy, http://www.mrcophth.com/focus1/Screening%20for%20Diabetic%20Retinopathy.htm.
8. S. Harding, L. Garvican, and J. Talbot, "The impact of national diabetic retinopathy screening on ophthalmology: the need for urgent planning," Eye, vol. 19, no. 9, pp. 1009-1011, 2005.
9. P. S. Silva, J. D. Cavallerano, L. M. Aiello, and L. P. Aiello, "Telemedicine and diabetic retinopathy moving beyond retinal screening," Archives of Ophthalmology, vol. 129, no. 2, pp. 236-242, 2011.
10. S. Garg and R. M. Davis, "Diabetic retinopathy screening update," Clinical Diabetes, vol. 27, no. 4, pp. 140-145, 2009.
11. P. Bragge, R. L. Gruen, M. Chau, A. Forbes, and H. R. Taylor, "Screening for presence or absence of diabetic retinopathy : a meta-analysis," Archives of Ophthalmology, vol. 129, no. 4, pp. 435-444, 2011.
12. O. R. Gibson, L. Segal, and R. A. McDermott, "A simple diabetes vascular severity staging instrument and its application to a Torres Strait Islander and Aboriginal adult cohort of north Australia," BMC Health Services Research, vol. 12, no. 1, article 185, 2012.
13. M. R. K. Mookiah, U. R. Acharya, C. K. Chua, C. M. Lim, E. Y. K. Ng, and A. Laude, "Computer-aided diagnosis of diabetic retinopathy: a review," Computers in Biology and Medicine, vol. 43, no. 12, pp. 2136-2155, 2013.
14. G. S. Sotland, S. Philip, A. D. Fleming et al., "Manual vs. automated: the diabetic retinopathy screening debate," Ophthalmology Times, vol. 4, no. 2, 2008.
15. M. Niemeijer, B. van Ginneken, M. J. Cree et al., "Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs," IEEE Transactions on Medical Imaging, vol. 29, no. 1, pp. 185-195, 2010.
16. M. Bouhaimed, R. Gibbins, and D. Owens, "Automated detection of diabetic retinopathy: results of a screening study," Diabetes Technology & Therapeutics, vol. 10, no. 2, pp. 142-148, 2008.
17. A. D. Fleming, K. A. Goatman, S. Philip, G. J. Prescott, P. F. Sharp, and J. A. Olson, "Automated grading for diabetic retinopathy: a large-scale audit using arbitration by clinical experts," British Journal of Ophthalmology, vol. 94, no. 12, pp. 1606-1610, 2010.
18. N. Larsen, J. Godt, M. Grunkin, H. Lund-Andersen, and M. Larsen, "Automated detection of diabetic retinopathy in a fundus photographic screening population," Investigative Ophthalmology and Visual Science, vol. 44, no. 2, pp. 767-771, 2003.
19. A. D. Fleming, S. Philip, K. A. Goatman, P. F. Sharp, and J. A. Olson, "Automated clarity assessment of retinal images using regionally based structural and statistical measures," Medical Engineering and Physics, vol. 34, no. 7, pp. 849-859, 2012.
20. A. Csutak, D. M. Silver, J. Tozsér et al., "Plasminogen activator inhibitor in human tears after laser refractive surgery," Journal of Cataract and Refractive Surgery, vol. 34, no. 6, pp. 897-901, 2008.
21. E. A. Heise and P. E. Fort, "Impact of diabetes on alphacrystallins and other heat shock proteins in the eye," Journal of Ocular Biology, Diseases, and Informatics, vol. 4, no. 1-2, pp. 62-69, 2011.
22. É. Csosz, P. Boross, A. Csutak et al., "Quantitative analysis of proteins in the tear fluid of patients with diabetic retinopathy," Journal of Proteomics, vol. 75, no. 7, pp. 2196-2204, 2012.
23. Z. Torok, T. Peto, E. Csosz et al., "Tear fluid proteomics multimarkers for diabetic retinopathy screening," BMC Ophthalmology, vol. 13, no. 1, article 40, 2013.
24. A. Berta, "Standardization of tear protein determinstions: the effects of sampling, flowrate, and vascular permeability," in The Preocular Tear Film in Health, Disease, and Contact Lens Wear, F. J. Holly, Ed., pp. 418-435, Dry Eye Institute, 1986.
25. M. J. Cree, J. A. Olson, K. C. Mchardy, P. F. Sharp, and J. V. Forrester, "A fully automated comparative microaneurysm digital detection system," Eye, vol. 11, no. 5, pp. 622-628, 1997.
26. G. Quellec, M. Lamard, P. M. Josselin, G. Cazuguel, B. Cochener, and C. Roux, "Optimalwavelet transformfor the detection of microaneurysms in retina photographs," IEEE Transactions on Medical Imaging, vol. 27, no. 9, pp. 1230-1241, 2008.
27. L. Vincent, "Morphological grayscale reconstruction in image analysis: applications and efficient algorithms," IEEE Transactions on Image Processing, vol. 2, no. 2, pp. 176-201, 1993.
28. J. H. Friedman, "Greedy function approximation: a gradient boosting machine," Annals of Statistics, vol. 29, no. 5, pp. 1189-1232, 2001.
29. Generalized Boosted Regression Models, http://cran.r-project.org/web/packages/gbm/index.html.
30. S. Srinivasan, M. Thangavelu, L. Zhang, K. B. Green, and K. K. Nichols, "iTRAQ quantitative proteomics in the analysis of tears in dry eye patients," Investigative Ophthalmology and Visual Science, vol. 53, no. 8, pp. 5052-5059, 2012.
31. B. Li, M. Sheng, L. Xie et al., "Tear proteomic analysisofpatients with type 2 diabetes and dry eye syndrome by two-dimensional nano-liquid chromatography coupled with tandem mass spectrometry," Investigative Ophthalmology & Visual Science, vol. 55, no. 1, pp. 177-186, 2014.
32. A. Karatzoglou, K. Hornik, A. Smola, and A. Zeileis, "Kernlab: an S4 package for Kernel methods in R," Journal of Statistical Software, vol. 11, no. 9, 2004.
33. Smartbiobank, http://smartbiobank.com/.
34. R: A language and environment for statistical computing, http://www.r-project.org/.
35. GNU Octave, http://www.gnu.org/software/octave/.
36. L. I. Kuncheva and C. J. Whitaker, "Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy," Machine Learning, vol. 51, no. 2, pp. 181-207, 2003.
37. P. Sollich and A. Krogh, "Learning with ensembles: how overfitting can be useful," in Advances in Neural Information Processing Systems, vol. 8, pp. 190-196, 1996.
38. J. M. Lee, P. M. McMahon, C. Y. Kong et al., "Cost-effectiveness of breast MR imaging and screen-film mammography for screening BRCA1 gene mutation carriers," Radiology, vol. 254, no. 3, pp. 793-800, 2010.
39. M. D. Abramoff, J. M. Reinhardt, S. R. Russell et al., "Automated early detection of diabetic retinopathy," Ophthalmology, vol. 117, no. 6, pp. 1147-1154, 2010.
40. K. Goatman, A. Charnley, L. Webster, and S. Nussey, "Assessment of automated disease detection in diabetic retinopathy screening using two-field photography," PLoS ONE, vol. 6, no. 12, Article ID e27524, 2011.