He Qingxin, Xia Wenfang
Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Arch Med Sci. 2024 Jul 25;21(2):374-382. doi: 10.5114/aoms/191297. eCollection 2025.
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.
肌肉减少症是一种临床综合征，其特征是骨骼肌质量和力量下降，会导致跌倒、骨折、虚弱、残疾和死亡率增加等不良事件。与双能X线吸收法（DXA）、生物电阻抗分析（BIA）和身体成分分析等先前的诊断技术相比，计算机断层扫描（CT）具有显著优势。由人工智能（AI）软件增强的机会性CT成像为肌肉减少症提供了一种更优的诊断工具。AI软件可以自动分割来自不同人群的机会性CT图像上的肌肉群，从而能够高效计算身体成分参数，并更准确、快速地诊断肌肉减少症。早期干预可能会显著降低与肌肉减少症相关的不良临床结局。本研究旨在评估与传统诊断技术相比使用CT图像的优势，并评估机会性CT图像上不同脊柱水平的骨骼肌参数对诊断肌肉减少症的价值。
