This study aims to present the possibility of using artificial intelligence techniques to classify diseases of the posterior segment of the eye, which are visible on type B OCT cross-sections. The datasets of retinal images available on the Internet are discussed, with particular emphasis on datasets useful for learning artificial neural networks, allowing for the automatic classification process. A simple yet user-friendly software called RePatClas has been developed that allows one to test the classification process for individual photos. To train the classification models, images from the publicly available LOCT dataset (version two) were used, which contains 84485 images of four classes: CNV, DME, DRUSEN, and NORMAL. The use of the RePatClas software is illustrated with examples. The prepared software allows the reader to familiarize themselves with the topic of current artificial neural network solutions.