Microwave renal denervation temperature prediction using hybrid machine learning: in silico 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.