Microwave renal denervation temperature prediction using hybrid machine learning: <i>in silico</i> evaluation using human body model

Abstract

Transcatheter renal denervation (RDN) has emerged as a novel treatment option to lower blood pressure (BP) by ablating the renal nerve. However, inconsistent results of failing to reduce BP were reported, primarily due to the inability to confirm ablation temperature at the treatment area. In order to address this, we proposed a microwave balloon catheter with a hybrid machine learning (ML) algorithm to achieve a deeper ablation and predict the temperature. Through an in silico evaluation using a human body model TARO, the proposed method achieved 1.5 • C difference compared to simulation using TARO, showing the proposed ML method capability.

High-Precision Temperature Analysis Considering Temperature-Dependent Tissue Properties in Renal Denervation

“…10 (a). In this study, the model was created by cutting out the area including the renal nerve, which is the treatment site for RDN, from the human model TARO and processing it in the same way as in previous studies [33]. A blood vessel model with an inner diameter of 6 mm and an outer diameter of 7 mm was designed at the center, and a catheter was inserted into the central axis of the blood vessel.…”

“…A schematic diagram of the inserted catheter is shown in Fig. 10 (b) [33]. The microwave catheter consisted of a flexible coaxial-slot antenna and a balloon.…”