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Artificial intelligence (AI) is rapidly entering modern medical practice. Many routine clinical tasks, from imaging and automated diagnostics to robotic surgery, cannot be imagined without the use of AI. Neural networks show impressive results when analyzing a large amount of data obtained from standard automated perimetry, optical coherence tomography (OCT) and fundus photography. Currently, both in Russia and abroad mathematical algorithms are being developed that allow detection of glaucoma based on certain signs. This article analyzes the advantages and disadvantages of employing artificial intelligence in ophthalmological practice, discusses the need for careful selection of the criteria and their influence on the accuracy of calculators, considers the specifics of using mathematical analysis in suspected glaucoma, as well as in an already established diagnosis. The article also provides clinical examples of the use of glaucoma risk calculator in the routine practice of an ophthalmologist.

Purpose. To estimate the accuracy of IOP measurement using artificial intelligence (AI) technologies and applanation tonometry with fixed strength. Material and methods. 290 patients (576 eyes) underwent applanation tonometry according to Maklakov with tonometer weights of 5, 10, and 15 g using a modified elastotonometry technique followed by an analysis of impression quality and diameter measurements by three independent ophthalmologist experts. The prints were then fed into a neural network to check the repeatability and reproducibility of the measurements. Results. The comparison of the diameters of the Maklakov tonometer prints determined by AI based on the neural network with the measurements data provided by three experts showed that neural network underestimates the measurement results by an average of 0.27 (-3.81; 4.35) mm Hg. At the same time, the intraclass correlation coefficient for all prints was 98.3%. The accuracy of diameter measurements of prints by neural network differs for tonometers of different weights, e.g. for a 5 g tonometer the difference was 0.06 (-3.38; 3.49) mm Hg, for 10 g and 15 g tonometers was 0.14 (-3.8; 3.51) and 0.95 (-3.84; 5.74) mm Hg, respectively. Conclusion. High accuracy and reproducibility of the measurements by the neural network, was shown to surpass the reproducibility of human-implemented measurements.

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Background — An increase in the frequency of manifestations of dry eye syndrome (DES), against the background of hypotensive therapy, leads to a decrease in treatment adherence. After reformulation in the composition of a preservative-free preparation of a carbonic anhydrase inhibitor (CAI) (sodium benzoate was added), patient complaints of burning sensation during instillation became more frequent, which was the reason for our study. Objective — To evaluate the effect of sodium benzoate on the course of DES during local hypotensive therapy of glaucoma. Methods and Results — Group 1 consisted of 21 eyes with glaucoma receiving hypotensive therapy, Group 2 (20 eyes) included patients with suspected glaucoma not receiving therapy. Patients underwent standard diagnostic methods and examination of the ocular surface condition: tear break-up time test sensu Norn, Schirmer test, vital staining with lissamine green, and a survey based on the Ocular Surface Disease Index (OSDI) questionnaire. When a preservative-free CAI in combination therapy was replaced with the CAI containing sodium benzoate, no statistically significant change in IOP occurred after four weeks of treatment (p> 0.05). In both groups, the indicators characterizing the ocular surface condition did not change statistically significantly over the observation period. Conclusion — Replacement of preservative-free CAI in combination therapy of glaucoma with CAI with sodium benzoate does not lead to statistically significant changes in intraocular pressure. The ocular surface condition does not change statistically significantly over a month of observation. Instillation discomfort is not related to the pH of the preparation.