Analysis of the current status of computed tomography diagnosis of sarcopenia

Abstract

Sarcopenia is a clinical syndrome characterized by the reduction of skeletal muscle mass and strength, leading to adverse events such as falls, fractures, frailty, disability, and increased mortality. Compared to previous diagnostic techniques such as dual-energy X-ray absorptiometry (DXA), bioelectrical impedance analysis (BIA), and body composition analysis, computed tomography (CT) offers significant advantages. Opportunistic CT imaging, enhanced by artificial intelligence (AI) software, provides a superior diagnostic tool for sarcopenia. AI software can automatically segment muscle groups on opportunistic CT images from different populations, enabling the efficient calculation of body composition parameters and more accurate and rapid diagnosis of sarcopenia. Early intervention may significantly reduce adverse clinical outcomes associated with sarcopenia. This study aims to evaluate the advantages of using CT images compared to traditional diagnostic techniques and to assess the value of skeletal muscle parameters at different spinal levels on opportunistic CT images for diagnosing sarcopenia.

Keywords: artificial intelligence; computed tomography; sarcopenia; skeletal muscle index.