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Diagnosis of Sarcopenia Using Convolutional Neural Network Models Based on Muscle Ultrasound Images: Prospective Multicenter Study

Diagnosis of Sarcopenia Using Convolutional Neural Network Models Based on Muscle Ultrasound Images: Prospective Multicenter Study

For example, Chen et al [23] developed a diagnostic model for sarcopenia based on age, cross-sectional area changes, and shear wave elasticity value changes, yielding an AUC of 0.883. Tang et al [24] developed an ultrasound-derived muscle assessment system based on muscle thickness, handgrip strength, and gait speed. The system had an overall diagnostic sensitivity of 92.7% and a specificity of 91% for sarcopenia.

Zi-Tong Chen, Xiao-Long Li, Feng-Shan Jin, Yi-Lei Shi, Lei Zhang, Hao-Hao Yin, Yu-Li Zhu, Xin-Yi Tang, Xi-Yuan Lin, Bei-Lei Lu, Qun Wang, Li-Ping Sun, Xiao-Xiang Zhu, Li Qiu, Hui-Xiong Xu, Le-Hang Guo

J Med Internet Res 2025;27:e70545