Monitoring Ablation Therapy Using Ensemble Learning Models


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Radiofrequency ablation is a minimally-invasive treatment method that aims to destroy undesired tissue by exposing it to alternating current in the 100 kHz to 800 kHz frequency range and heating it until it is destroyed via coagulative necrosis. Ablation treatment is gaining momentum especially in cancer research, where the undesired tissue is the malignant tumors. While ablating the tumor with an electrode or catheter is an easy task, monitoring the ablation process is a must in order to maintain the reliability of the treatment. Common methods for this monitoring task have proven to be accurate, however they are all time-consuming, which makes the ablation process in real life more cumbersome and expensive due to the time-dependent nature of the clinical procedure. In this study, a Machine Learning (ML) approach is presented to reduce the monitoring time while keeping the accuracy of the conventional methods. Different setups are used to perform the ablation and collect impedance data at the same time and different ML algorithms are tested to predict the ablation depth in 3D, based on the collected data. In the end, it is shown that an optimal pair of hardware setup and ML algorithm is able to control the ablation by estimating the lesion depth within an average of micrometer-magnitude error range while keeping the estimation time within 5 seconds.

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