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 ( $\sigma $ ) were significantly different among the three groups (p <0.05).> $\sigma $ (r = 0.73, $\text{R}^{{2}} =~~0.54$ , p <0.001),> $\text{R}^{{2}} =~~0.27$ , p <0.05).> $\sigma $ was positively correlated with hand grip strength (HGS) (r = 0.52, $\text{R}^{{2}} =~0.20$ , p= 0.30), and maximum voluntary muscle contraction (MVC) (r= 0.73, $\text{R}^{{2}} =~~0.49$ , p<0.001),> $\text{R}^{{2}} =~~0.56$ , p<0.001)> $\text{R}^{{2}} =~0.57$ , 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.