Real-time estimation of lesion depth and control of radiofrequency ablation within ex vivo animal tissues using a neural network

Abstract

The results show that a NN could make lesion depth estimations in real-time using less in situ devices than current techniques. With the NN-based technique, physicians could deliver quicker and more precise ablation therapy.

Real-time monitoring radiofrequency ablation using tree-based ensemble learning models

“…The ablation is performed and complex impedance data were collected using the tissue model and the RFA hardware setup as in Wang et al's study [31]. The model consisted of pork loin and pork belly, simulating breast tissue.…”

“…These measurement peripherals were controlled by a Â86-64 microprocessor-based workstation (Supermicro, San Jose, CA) with 16 GB of memory. This first dataset that was collected with this equipment and on which the results of Wang et al's study [31] are based, is named as 'first instrumentation data'.…”

“…The work most related to this study would be a pseudo-EIT method published in Wang et al's study [31] that utilizes electrical impedance, but rather than reconstruct the entire model as with EIT, an ANN was used as a depth estimation system that approximates the lesion depth map solution. The ensemble models that will be introduced in this study are predicted to outperform the ANN model for reasons that are thoroughly discussed in Section 2.2.…”

“…The ensemble models that will be introduced in this study are predicted to outperform the ANN model for reasons that are thoroughly discussed in Section 2.2. Another extension of this study is using regression which directly predicts the ablation depth as opposed to the classification model in Wang et al's study [31]. Section 2.2 explains why this is a safer approach than classification for a real-life scenario of monitoring ablation therapy.…”

“…Section 2.2 explains why this is a safer approach than classification for a real-life scenario of monitoring ablation therapy. For comparison reasons, the ANN in Wang et al's study [31] is retrained as a regression model alongside with different network architectures. The results from all the models will be presented and compared in Section 3.…”