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Potential Applications of Artificial Intelligence in Muscle Tissue Assessment by Computed Tomography Images: a Literature Review

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Abstract

Background: Syndromes and diseases in which the qualitative and quantitative composition of the human body change are receiving increasing attention. Sarcopenia is a disease characterized by generalized loss of muscle mass and strength, affecting both able-bodied and elderly populations, with a global prevalence in the general population of up to 10% according to the literature. According to the 2019 European Working Group on Sarcopenia in the Elder People consensus, the gold standard of medical imaging for the assessment of muscle mass loss is computed tomography (CT) and magnetic resonance imaging (MRI). With the increasing use of artificial intelligence (AI) technologies, there is an opportunity to analyze large amounts of medical data, including CT images.

Purpose: To acquaint the general audience with the current work on the medical imaging of significant changes in skeletal muscle tissue from CT images using AI technologies, including highlighting the available options for their clinical and scientific application.

Search and selection methodology: Publications have been searched by advanced search query in bibliographic databases PubMed and eLibrary.ru.

Results: 46 selected original articles published between 2019 and 2024 have been analyzed.

The variants of clinical and scientific application of AI algorithms are reviewed. The main purpose of clinical application is to assess the prognostic value of morphometric indices of sarcopenia for a wide range of diseases – oncological (most of the works) and chronic, as well as for conditions after surgical interventions. The acquisition of additional morphometric indices of not only muscle but also adipose tissue were noted in works where it had been carried out and had clinical significance. The main problem existing at present time is highlighted, which is the lack of a clear place in the clinical diagnostic paradigm. The main option for scientific application is the processing of large amounts of data for population studies. Details of the methodology of CT body composition assessment, including the most commonly used skeletal muscle index thresholds for CT diagnosis of sarcopenia, are given, and the technical aspects of the AI algorithms used were summarised. In conclusion, the high interest of researchers in this topic was noted, and prospects for further research in this area and application in practice were outlined.

About the authors

A. K. Smorchkova

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: SmorchkovaAK@zdrav.mos.ru
Moscow

A. V. Petraikin

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: SmorchkovaAK@zdrav.mos.ru
Moscow

Yu. A. Vasilev

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: SmorchkovaAK@zdrav.mos.ru
Moscow

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