Sarcopenia is a group of clinical syndromes characterized by decreased skeletal muscle mass and strength. The incidence of sarcopenia in the elderly population remains high, which can lead to many adverse events such as falls, fractures, weakness, and also increases the risk of disability and death, so clear diagnosis and early intervention are essential. Computed tomography (CT) has significant advantages over previous diagnostic techniques such as dual-energy X-ray absorptiometry (DXA), bioelectrical impedance analysis (BIA), and body composition analysis, and has been increasingly used to diagnose sarcopenia. CT images use artificial intelligence (AI) software to automatically segment muscle groups and calculate body composition parameters, which is convenient and fast for diagnosing sarcopenia. In order to increase the effectiveness of sarcopenia clinical diagnosis, this study aimed to evaluate the benefits of CT compared to other diagnostic techniques and the value of skeletal muscle parameters at various spinal levels on CT images.