Machine Learning Algorithms for the Analysis of Age-Related Macular Degeneration Based on Optical Coherence Tomography: a Systematic Review

Ekaterina A. Lopukhova orcid (Login required)
Ufa University of Science and Technology, Russia

Rada R. Ibragimova orcid
Bashkir State Medical University, Ufa, Russia
CJSC “Optimedservice”, Sterlitamak, Russia

Gulnaz M. Idrisova orcid
Bashkir State Medical University, Ufa, Russia
CJSC “Optimedservice”, Sterlitamak, Russia

Irina A. Lakman orcid
Ufa University of Science and Technology, Russia
Bashkir State Medical University, Ufa, Russia

Timur R. Mukhamadeev orcid
Bashkir State Medical University, Ufa, Russia
CJSC “Optimedservice”, Sterlitamak, Russia

Elizaveta P. Grakhova
Ufa University of Science and Technology, Russia

Azat R. Bilyalov orcid
Bashkir State Medical University, Ufa, Russia

Ruslan V. Kutluyarov orcid
Ufa University of Science and Technology, Russia


Paper #3570 received 7 Dec 2022; revised manuscript received 6 Mar 2023; accepted for publication 8 Mar 2023; published online 28 Apr 2023.

DOI: 10.18287/JBPE23.09.020202

Abstract

Age-related macular degeneration (AMD) is one of the leading causes of irreversible blindness. Every year, there is an increase in the number of patients with AMD worldwide. To date, the primary method in diagnosing AMD is optical coherence tomography (OCT), which provides the most visual data for identifying disease biomarkers. However, a growing volume of research requires optimizing the work of an ophthalmologist to minimize diagnostic errors. In this regard, the study aimed at integrating computer vision applications into the OCT image processing system is gaining popularity since it allows not only to identify images with the most likely presence of AMD but also to determine the stages of this disease, localize biomarkers and obtain a prognosis for the dynamics of its development. The variety of such approaches is expressed in the application of various machine learning algorithms, metrics for evaluating their effectiveness, sources of input information, and work verification. This statistical review analyzes examples of works devoted to computer vision algorithms in the study of OCT images for diagnosing, staging, or predicting the dynamics of AMD and highlights the features and trends within this area.

Keywords

optical coherence tomography; age-related macular degeneration; machine learning; deep learning

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