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Purpose The aim of the project was to develop a methodology for diabetic retinopathy (DR) screening based on the examination of tear fluid biomarker changes. To evaluate the usability of protein biomarkers for pre-screening purposes different approaches and machine learning algorithms were used. Methods All persons involved in the study had diabetes. DR was diagnosed by capturing 7-field fundus images. 165 eyes were examined, 55 were diagnosed healthy and 110 images showed signs of DR. Tears were taken from all eyes and state-of-the-art nano-HPLC coupled ESI-MS/MS mass spectrometry protein identification was performed on them. Applicability of protein biomarkers was evaluated by six different optimal parameterized machine learning algorithms. Results Out of the six identified machine learning algorithms, result of the Recursive Partitioning proved to be the most accurate. The performance indicators of the system applying the above algorithm indicated 74 % sensitivity and 48% specificity. Conclusion Neither protein biomarkers nor machine learning algorithms are recommended alone for screening purposes because of low specificity and sensitivity values. This tool can be preferably used to improve the results of image processing methods as a complementary tool in automatic or semiautomatic systems.

Purpose To investigate the hypothesis that the plasminogen activator proteolysis cascade in the eye is controlled locally instead of systemically. During pregnancy, systemic blood levels of estradiol (E2), progesterone (P4) and plasminogen activator inhibitors (PAI) are expected to rise with gestational age; is there a consequent rise in PAI normally present in tears? Methods PAI-2 levels were measured, using Imubind ELISA, longitudinally from three to four tear samples collected from each of 13 women between 8 and 36 weeks of pregnancy. During the same visits, blood samples were analyzed for PAI-2, E2 and P4. The analysis included successful results from 36 tear samples (PAI-2) and 45 blood samples (PAI-2, E2 and P4). Results Increases in blood levels of PAI-2, E2 and P4 occurred over the measured gestational period, significantly correlated with time and with each other (all R2>0.75). Tear levels of PAI-2 did not increase and were not correlated with gestation or blood PAI-2 (both R2

Purpose Diabetic retinopathy (DR) is among the most common causes of blindness in the developed world. Since many patients retain normal vision and may experience only minimal reduction despite the presence of a severe, sight-threatening disease, screening and early detection of DR is highly recommended. Our purpose was to develop an automated DR screening system to gain computational support for high throughput screening activities. Methods Detection of microaneurysms (MA) - the most important indicators and the earliest signs of DR - and of other lesions (exudates, vascular structures, etc.) have been assembled in an ensemble-learning based framework, which has been tested on 1200 images of the Messidor database. Results Images were classified as having DR or non by the MA detector only with 95% sensitivity, 51% specificity and 76% accuracy. By including other lesions, our system reached 89% sensitivity, 82% specificity and 85% accuracy. Conclusion Our system has sensitivity/specificity values for the MA step comparable to or better then other automated systems available, as tested in the Retinopathy Online Challenge, where it is currently ranked as first. MA detection remains a key component in automated DR screening, but detection of other DR lesions can lead to further improvement. This work was supported in part by NKTH, TECH08-2, Hungary DRSCREEN project and the NIHR BMRC in Ophthalmology (TP).