## 4 Discussion  
The developed hybrid classifier has demonstrated high efficiency and aligns well with expert assessments. The method's potential clinical applicability goes beyond the initial testing environment, opening up prospects for use in various ophthalmological conditions and diseases. This method is desirable for diseases with stages with different manifestation frequencies in patients, leading to a significant class imbalance during the training phase of classifier development. Integrating these classifiers into the CDSS element will help magnosticians enhance diagnostic accuracy and optimize workflow management. This approach eliminates the need for a detailed marking of medical image sets while also emphasizing the importance of gathering sufficient examples of rare disease stages. Furthermore, the classifier's ability to accurately differentiate between sub-stages of or variations in ophthalmic conditions can facilitate earlier interventions, potentially preventing the progression to more severe forms of the disease and ultimately improving patient treatment outcomes. However, expanding clinical applicability requires solving several problems related to integration into clinical practice. One major challenge is ensuring an equal or comparable amount of data from various tomograph models, as OCT images have different visualization features. Machine learning models included in the hybrid classifier must operate with a sufficient set of visual features that differ from each other to generalize them into one class. This presents a significant limitation for developing the hybrid classifier, as it cannot analyze OCT images obtained from tomographs that were not part of the training sample. The same applies to the visual quality of OCT images, which can decrease the performance of the hybrid classifier due to the suboptimal image quality often encountered in real clinical settings. Reducing the criticality of this factor can be achieved by introducing reliable preprocessing methods and including mechanisms for evaluating and managing image quality. This can increase the classifier's stability, something to which future research efforts will be devoted in further improving the presented method of developing hybrid classifiers. One of the challenges in integrating the proposed method for developing a hybrid classifier is the \"black box\" nature of the deep learning models it utilizes. This lack of transparency can reduce trust among clinicians. Although the classifier demonstrates high efficiency, its interpretability issues may make clinicians hesitant to rely on automated recommendations entirely. To address this concern, future work will focus on iterating the algorithms to enhance the proposed method, incorporating techniques from explainable artificial intelligence. This approach will help clinicians better understand the classifier's decision-making process. When comparing the effectiveness and methodological approaches of the presented hybrid classifier for identifying AMD stages with previous studies, several notable differences and similarities emerge. The proposed hybrid classifier exhibits impressive performance metrics, achieving an accuracy of over 96%, along with sensitivity and specificity levels surpassing those reported in prior research [29]. Additionally, the hybrid classifier demonstrates advanced recognition capabilities, as evidenced by its high F1-score and AUC values. While the authors of [29] achieved commendable accuracy with CNN-based screening, they encountered limitations when working with unbalanced classes, often resulting in biased predictions favoring the majority classes. Unlike reference [29], where traditional deep convolutional neural networks were used and designed mainly for processing fundus images, the method described in this article combines hybrid and hierarchical classification approaches, effectively addressing the class imbalance characteristic of AMD datasets.