Concept of Information Technology for Diagnosis and Prognosis of Glaucoma Based on Machine Learning Methods - read online free

Aug 3, 2026

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Early and automated glaucoma diagnosis and prognosis are difficult with conventional clinical workflows because many existing machine- and deep-learning tools remain costly, complex to develop and use, limited in adoption, and still require an ophthalmologist to interpret outputs. The study aims to build information technology grounded in machine learning that uses fundus retinal images and optical coherence tomography to run automated processing with low resource requirements and mass-use feasibility. It targets early-stage detection by classifying eyes as normal or glaucomatous.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Abstract

Problem and motivation

Limits of existing machine learning tools

Proposed information technology

Data sources: fundus and optical coherence tomography

Automated early classification: normal vs glaucomatous

Background and epidemiology of glaucoma

Global prevalence and burden

Clinical challenge in early detection

Symptom delay and progressive course

City Research Online City, University of London Institutional Repository | Citation: Kysil, V. , Popov, P. T. , Drachuk, O. , Hnenna, V. & Martyniuk, I. (2024) . Concept of Information Technology for Diagnosis and Prognosis of Glaucoma Based on Machine
Learning Methods. CEUR Workshop Proceedings, 3675, pp. 171-181. ISSN 1613-0073 This is the published version of the paper.
This version of the publication may differ from the final published version. | | --- | | Permanent repository link: [https://openaccess.city.ac.uk/id/eprint/33029/](https://openaccess.city.ac.uk/id/eprint/33029/)[ ](https://openaccess.city.ac.uk/id/eprint/33029/)[Link to published version](Link to published version:)[:](Link to published version:)
Copyright: City Research Online aims to make research outputs of City, University of London available to a wider audience. Copyright and Moral Rights remain with the author(s) and/or copyright holders. URLs from City Research Online may be freely distributed and linked to.
Reuse: Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content isnot changed in any way. | | City Research Online: | [http://openaccess.city.ac.uk/](http://openaccess.city.ac.uk/) | [publications@city.ac.uk](publications@city.ac.uk) | | --- | --- | --- | | | | | ⋆ Volodymyr Kysil1,∗,†, Peter T. Popov2,†, Olga Drachuk3,†, Valentyna Hnenna3,† and Inna Martyniuk1,† 1 Khmelnytskyi National University, Institutska str., 11, Khmelnytskyi, 29016, Ukraine 2 City University of London, Northampton Square, London, EC1V 0HB, United Kingdom 3 National Pirogov Memorial Medical University, Pirogova str., 56, Vinnytsya, 21018, Ukraine Abstract The current challenge is early and automated diagnosis and prognosis of glaucoma using information technology based on machine and deep learning methods. The conducted analysis of the methods and tools for diagnosing and predicting the glaucoma has shown that now there are many such methods and tools, including those based on machine learning and deep learning, but all of them have certain drawbacks, such as their "niche" (lack of mass use, development of tools exclusively for proving and testing the theoretical positions developed by the authors), complexity of development, complexity of use, high cost, the need for an ophthalmologist to decipher the data obtained, etc. Therefore, the aim of this study is to develop the information technology for diagnosis and prognosis of glaucoma based on machine learning methods, which will have minimal requirements and resource needs, be characterized by low cost and mass use, and will not require an ophthalmologist to decipher the data generated by the neural network. The proposed information technology for diagnosis and prognosis of glaucoma based on machine learning methods automates the processing of fundus retinal images and optical coherence tomography images based on machine learning in order to automatically diagnose glaucoma at early stages by classifying the eye as normal or glaucomatous. Keywords glaucoma, glaucoma diagnosis, glaucoma prognosis, machine learning, information technology. Globally, glaucoma is the second most common cause of blindness and subsequent disability [1, 2]: IntelITSIS’2024: 5th International Workshop on Intelligent Information Technologies and Systems of Information Security, March 28, 2024, Khmelnytskyi, Ukraine ∗ Corresponding author. † These authors contributed equally. [vovikusspambox@gmail.com](vovikusspambox@gmail.com) (V. Kysil); [p.t.popov@city.ac.uk](p.t.popov@city.ac.uk) (P. Popov); [drachuk@vnmu.edu.ua](drachuk@vnmu.edu.ua) (O. Drachuk); [valentina.gnenna@gmail.com](valentina.gnenna@gmail.com) (V. Hnenna); [Inmartunyk@ukr.net](Inmartunyk@ukr.net) (I.

FAQs

What problem does the proposed system address for glaucoma care?

It targets early and automated diagnosis and prognosis of glaucoma to reduce delays in detection and support earlier intervention through automated image processing.

Which machine learning inputs are used to perform diagnosis in the study?

The technology automates processing of fundus retinal images and optical coherence tomography images to classify eyes as normal or glaucomatous at early stages.

Why do the authors argue current machine/deep learning tools are not widely used?

They cite drawbacks such as niche adoption, high development and use complexity, high costs, and the need for ophthalmologists to decipher the neural network outputs.

Aug 3, 2026

Upload by Eliana