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

作者：Zi‐Tong Chen, Xiao‐Long Li, Feng-Shan Jin, Yi-Lei Shi, Lei Zhang, Haohao Yin, Yuli Zhu, Xinyi Tang, Xi-yuan Lin, Bo Lü, Qun Wang, Liping Sun, Xiao Xiang Zhu, Li Qiu, Hui‐Xiong Xu, Le‐Hang Guo · 发表于：Journal of Medical Internet Research · 年份：2025 · DOI：10.2196/70545 · 被引用次数：10 · 研究领域：Nutrition and Health in Aging、Body Composition Measurement Techniques、Dysphagia Assessment and Management

BACKGROUND: Early detection is clinically crucial for the strategic handling of sarcopenia, yet the screening process, which includes assessments of muscle mass, strength, and function, remains complex and difficult to access. OBJECTIVE: This study aims to develop a convolutional neural network model based on ultrasound images to simplify the diagnostic process and promote its accessibility. METHODS: This study prospectively evaluated 357 participants (101 with sarcopenia and 256 without sarcopenia) for training, encompassing three types of data: muscle ultrasound images, clinical information, and laboratory information. Three monomodal models based on each data type were developed in the training cohort. The data type with the best diagnostic performance was selected to develop the bimodal and multimodal model by adding another one or two data types. Subsequently, the diagnostic performance of the above models was compared. The contribution ratios of different data types were further analyzed for the multimodal model. A sensitivity analysis was performed by excluding 86 cases with missing values and retaining 271 complete cases for robustness validation. By comprehensive comparison, we finally identified the optimal model (SARCO model) as the convenient solution. Moreover, the SARCO model underwent an external validation with 145 participants (68 with sarcopenia and 77 without sarcopenia) and a proof-of-concept validation with 82 participants (19 with sarcopenia and 63 witho...