POSSIBILITIES OF USING NEURAL NETWORK ANALYSIS IN THE DIAGNOSIS OF DRY EYE SYNDROME

The prevalence rate of dry eye syndrome varies from 6.5 to 95 %. Diagnostic criteria are based on different methods and/or their combinations and are characterized by heterogeneity. The aim of the study. To identify the risk factors for the development of dry eye syndrome in order to create a technology for early diagnosis of the degree of the disease in young people without concomitant ocular and general somatic pathology. Materials and methods. Fifty patients aged 24 [22; 27] years were examined. We carried out an ophthalmological examination, including autorefractometry, visometry, biomicroscopy, the Norn test, a survey using the author’s questionnaire, andanassessmentofthedegreeofdryeyesyndromeusingtheOcularSurfaceDisease Index (OSDI). Three study groups were formed: control group (OSDI = 0–13 points); group 1– patients withOSDI= 14–22 points; group 2– patients withOSDI>22 points. Results. When examining presented independent variables, screen time had the highest normalized importance (100 %), followed by tear film breakup time (58.4%), smoking (24.3%), nightshifts (22.5%)andusing softcontactlenses (11.1%). The technology for early diagnosis ofthe degree ofdry eye syndrome is implemented on the basis ofa multilayer perceptron, the percentage ofincorrect predictions during its training process was 8.0 %. The structure of the trained neural network included 8 input neurons (the value of screen time and tear film breakup time, the presence or absence ofsmoking, night shifts and/or the use ofsoft contact lenses), two hidden layers containing 3 and 2 units, respectively, and 3 output neurons. Conclusion. The proposed neural network has no difficulties in assessing the early diagnosis of the severity of dry eye syndrome and can be used in clinical practice.