Quantitative Assessment and Diagnosis of Muscle Function in Sarcopenia Based on EIT-derived Parameters.

The quantitative evaluation and diagnosis of muscle function in patients with sarcopenia are crucial to mitigate functional decline and the health burden in aging populations. This study proposed a method for the classification of sarcopenia and the evaluation of muscle function scores based on EIT technology. We recruited 31 participants, including individuals with sarcopenia (n = 11), healthy elderly (n = 10), and healthy young adults (n = 10), obtained muscle clinical fitness assessment scores and EIT-derived parameters, conducted intergroup comparisons of EIT parameters and clinical scores, and constructed a machine learning classification model for sarcopenia. EIT parameters conductivity (σ) were significantly different among the three groups (p < 0.05). Clinical muscle function scores showed a strong positive correlation with the σ (r = 0.73, R² = 0.54, p < 0.001), while negatively correlated with impedance (Z) (r = -0.55, R² = 0.27, p < 0.05). In addition, σ was positively correlated with hand grip strength (HGS) (r = 0.52, R² =0.20, p=0.30), and maximum voluntary muscle contraction (MVC) (r=0.73, R² = 0.49, p<0.001), and negatively correlated with age (r = -0.76, R² = 0.56, p<0.001) and SARC-F scale scores (r = -0.73, R² =0.57, p<0.001). Finally, the KNN-based sarcopenia classification model demonstrated strong performance in classification tasks, as evidenced by an accuracy of 0.89 and an AUC of 0.94. This study demonstrates that the EIT is a portable, wearable, and long-term monitoring tool for assessing and classifying muscle function in sarcopenia. With further clinical validation, it is expected to be used for early screening and rehabilitation monitoring of sarcopenia.