## 1 Introduction  
Age-related macular degeneration (AMD) is a socially significant disease associated with the risk of central vision loss. According to 2020 data, the prevalence of AMD worldwide is about 200 million cases [1]. It is important to note that AMD is a chronic disease that tends to progress gradually [2, 3]. Timely detection and treatment of AMD can help slow its progression and improve patients' quality of life [4, 5]. Monitoring the development of AMD is essential for addressing this issue [6]. AMD has three stages: early, intermediate, and late [7], each with distinct clinical presentations and biomarkers. The early stage is identified by drugs up to 125 \(\upmu\)m in size and is often asymptomatic, making it challenging to diagnose [8]. Signs of the intermediate stage include druses with a diameter of more than 125 \(\upmu\)m, drusenoid detachment of the pigment epithelium, and atrophy of the pigment epithelium outside the center. Complaints may also be absent at this stage [9]. The late stage is marked by deterioration of central vision, visual distortions, and changes in color perception. At this stage, geographic atrophy (GA) or macular neovascularization (MNV) may develop [10; 11; 12]. GA is the outcome of the early stage of AMD. MNV, if untreated, can lead to subretinal fibrosis (SF) [12; 13]. The tactics of managing patients with different stages of AMD differ significantly [14]. There is no specific treatment for the early stage of AMD; preventive measures are used to eliminate risk factors for the development and progression of AMD [15; 16]. Currently, drugs are being introduced to treat GA, but this therapy has yet to see widespread use [17]. In turn, treatment of the late form utilizes intravitreal injections to inhibit the vascular endothelial growth factor (anti-VEGF). These injections are widely utilized and have proven their effectiveness. It is crucial to identify an intermediate stage that separates the early and late stages to make timely adjustments to patients' diagnostic and treatment plans. This can help reduce or neutralize the negative factors in the development of the disease [18; 19]. A key aspect of analyzing the progression of AMD is tracking the moment when intravitreal injections of anti-VEGF drugs are needed to help slow down the progression of the late form of AMD [20]. However, distinguishing the intermediate stage of AMD from the early and late stages (geographic atrophy and macular neovascularization) can be difficult due to the similarity of biomarkers. The diagnostic needs to invest extra time and effort in visually identifying each biomarker and measuring its dimension, before comparing it with the evaluation scale. A large flow of patients can reduce efficiency due to human factors [21; 22]. The most informative and standard diagnostic method for AMD is optical coherence tomography [23]. Currently, computer vision (CV) and machine learning (ML) methods are widely used to automate optical coherence tomography (OCT) visual analysis [24]. Many studies use ML to identify the intermediate stage [25]. In this case, two main approaches to the implementation of staging algorithms can be distinguished:  
1. Direct classification of stages without identifying disease biomarkers [26; 27; 28; 29]. 2. Classification of AMD biomarkers identified in a retinal image using an additional CV algorithm. The biomarker extraction algorithm can be implemented using retinal segmentation based on unsupervised learning [30; 31; 32] or using supervised learning algorithms by comparing medical images and a segmented set [33; 34; 35]. Biomarker-based AMD classification offers several advantages. It provides a better understanding of the algorithm and expands its scope of application. Biomarker segmentation can also be used separately in the image classification pipeline to extract the position and shape of detected biomarkers. This information can be valuable for a quantitative and statistical analysis of pathologies [36; 37].