Publication date: 2023

DOI: 10.18287/JBPE23.09.020202

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. © 2023 Journal of Biomedical Photonics & Engineering.

Издатель: Samara National Research University

Тип: Article