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The possibilities of artificial intelligence use in the assessment of Maklakov intraocular pressure prints

https://doi.org/10.25700/NJG.2020.01.03

Abstract

OBJECTIVE: To assess the accuracy of Maklakov intraocular pressure imprints measurement by doctors and artificial intelligence.

MATERIALS AND METHODS: Two pairs of tonograms were prepared, obtained by a Maklakov tonometer with a load of 10.0 g. The tonograms were labeled anonymously, using a measuring ruler devised by prof. B.L. Polyak for 4 Maklakov tonometers. In total, 57 ophthalmologists took part in the work. A total of 40 prints were chosen based on there quality. The same prints were photographed by a Xiaomi mi smartphone camera 40 times with a different level of illumination and a different angle of rotation relative to the normal of the lens focal plane. Received photos in jpg format were analyzed by http://ai-tonometry.com algorithms and processed by the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm to extract sharper boundaries, and then translated into binary matrices (matrices consisting of “0” and “1”).

RESULTS: An imprint with a maximum number of measurements of 40 and a collegially accepted reliable tonometric level of 17 mmHg was measured in the middle range of 16.48±2.7 16.0 (15.0; 17) mmHg by doctors, and 17.0±1.1 17.0 (16.0; 17.0) mmHg by the neural network. At the same time, the range of imprint diameter measurements by the neural network was almost three times smaller, than human measurements.

CONCLUSION: The artificial intelligence-based mobile application allows for a high-quality monitoring of intraocular pressure and rejects prints of unsatisfactory quality, which may potentially reduce the number of patients with glaucoma progression.

About the Authors

D. A. Dorofeev
Chelyabinsk Public Clinical Hospital of No. 2
Russian Federation
M.D.


L. O. Vizgalova
The South Ural State Medical University of The Ministry of Health of the Russian Federation
Russian Federation
Resident


A. V. Gorobets
South Ural State University (National Research University)
Russian Federation
Student


E. V. Kanafin
Chelyabinsk Regional Multipurpose Boarding School for Gifted Children
Russian Federation
High school student


S. I. Kurochkin
South Ural State University (National Research University)
Russian Federation
Student


D. Yu. Vasilenko
LTD Aplit
Israel
Frontend developers


K. A. Efimova
Chelyabinsk Public Clinical Hospital of No. 2
Russian Federation
OCT operator


A Z. Ciganov
The South Ural State Medical University of The Ministry of Health of the Russian Federation
Russian Federation
Student


I. V. Kozlova
Scientific Research Institute of Eye Diseases
Russian Federation
Ph.D., Research Associate of Glaucoma Department


E. V. Karlova
Eroshevskiy Eye Hospital
Russian Federation
Ph.D., Head of Glaucoma Department


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Review

For citations:


Dorofeev D.A., Vizgalova L.O., Gorobets A.V., Kanafin E.V., Kurochkin S.I., Vasilenko D.Yu., Efimova K.A., Ciganov A.Z., Kozlova I.V., Karlova E.V. The possibilities of artificial intelligence use in the assessment of Maklakov intraocular pressure prints. National Journal glaucoma. 2020;19(1):20-27. (In Russ.) https://doi.org/10.25700/NJG.2020.01.03

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ISSN 2078-4104 (Print)
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