This article presents an algorithm implementing a radiomics approach to processing computed tomography (CT) data for diagnosing sarcopenia. The proposed method includes region of interest extraction, automatic muscle segmentation using deep learning models, extraction of radiomic features from CT-images, construction of correlation matrices, and selection of criteria for classification. The results show that the obtained radiomic parameters have a significant correlation with the presence of sarcopenia, allowing the construction of accurate classification models based on machine learning. This approach can significantly improve the diagnosis of sarcopenia, providing reliable non-invasive analysis methods.