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

Abstract

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.