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Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/70545, first published .
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

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

Journals

  1. Zhang M, Zhong M, Cheng Y, Zhang T. Intelligent Prediction Platform for Sepsis Risk Based on Real-Time Dynamic Temporal Features: Design Study. JMIR Medical Informatics 2025;13:e74940 View
  2. Filippucci E, Cipolletta E. Automated ultrasound in rheumatology: the dawn of a new era. RMD Open 2025;11(4):e006273 View
  3. Pei T, Lei Y, Gao Y, Zhang M, Xu T, Yang W, Wen Q, Liu Q. Artificial intelligence-driven assessment of sarcopenia in orthopedic geriatrics: technical progress and clinical implications. Frontiers in Endocrinology 2026;17 View
  4. Ucdal M, Gecegelen E, Balcı C. Multimodal Large Language Models: A Promising Frontier for Sarcopenia Diagnosis in Post-Acute and Long-Term Care. Journal of the American Medical Directors Association 2026;27(6):106192 View

Conference Proceedings

  1. Tilak S, Joshi P. 2025 IEEE Pune Section International Conference (PuneCon). Evaluating the Impact of Handcrafted Feature Fusion with CNN Architectures on Ultrasound Based Muscle Health Assessment View