Acta Biomedica Scientifica (Jun 2024)
Possibilities of using neural network analysis in the diagnosis of dry eye syndrome
Abstract
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, and an assessment of the degree of dry eye syndrome using the Ocular Surface Disease Index (OSDI). Three study groups were formed: control group (OSDI = 0–13 points); group 1 – patients with OSDI = 14–22 points; group 2 – patients with OSDI > 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 %), night shifts (22.5 %) and using soft contact lenses (11.1 %). The technology for early diagnosis of the degree of dry eye syndrome is implemented on the basis of a multilayer perceptron, the percentage of incorrect 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 of smoking, night shifts and/or the use of soft 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.
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