A Study on Endoscopic Radiofrequency Resection Based on Tissue-Adaptive Algorithm

Abstract Endoscopic mucosal resection (EMR) and endoscopic submucosal dissection (ESD) are widely used electrosurgical techniques for removing gastrointestinal polyps. However, these procedures often lead to complications such as bleeding, perforation, luminal strictures, and inflammation, primarily due to excessive thermal injury. In this study, we developed a radio frequency (RF) energy device capable of focusing energy more precisely at the cutting site. An impedance-based adaptive algorithm is implemented to dynamically adjust output power, thereby reducing thermal damage during tissue resection. In this study, the cutting resistance exerted on the electrode and the tissue temperature during cutting were measured, and histological sections were used to evaluate the extent of thermal injury caused during the cutting process. Under constant power outputs of 40 W, 50 W, and 60 W, average cutting resistance and temperature were recorded. Results indicate a negative correlation between output power and cutting resistance, as well as a positive correlation between output power and cutting temperature. Additionally, we compared performance between constant power and adaptive power modes at 50 W: the average cutting resistance and temperature were 0.110 ± 0.009 N and 115.6 ± 11.6 °C for the constant power mode, versus 0.096 ± 0.013 N and 107.7 ± 9.4 °C for the adaptive power mode. These findings demonstrate that the impedance-based adaptive algorithm effectively reduces tissue adhesion and thermal damage during cutting.