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Int J Hyperthermia. 2019;36(1):428-437. doi: 10.1080/02656736.2019.1587008. Epub 2019 Apr 2.

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

Author information

1
a Department of Electrical and Computer Engineering , Northwestern University , Evanston , IL , USA.
2
b Innoblative Designs , Chicago , IL , USA.
3
c Department of Biomedical Engineering , Northwestern University , Evanston , IL , USA.

Abstract

BACKGROUND:

Radiofrequency ablation is a minimally-invasive treatment method that aims to destroy undesired tissue by exposing it to alternating current in the 100 kHz-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 a malignant tumor. While ablating the tumor with an electrode or catheter is an easy task, real-time 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 or require expensive equipment, which makes the clinical ablation process more cumbersome and expensive due to the time-dependent nature of the clinical procedure.

METHODS:

A machine learning (ML) approach is presented that aims to reduce the monitoring time while keeping the accuracy of the conventional methods. Two different hardware 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 3 dimensions, based on the collected data.

RESULTS:

Both the random forest and adaptive boosting (adaboost) models had over 98% R2 on the data collected with the embedded system-based hardware instrumentation setup, outperforming Neural Network-based models.

CONCLUSIONS:

It is shown that an optimal pair of hardware setup and ML algorithm (Adaboost) is able to control the ablation by estimating the lesion depth within a test average of 0.3mm while keeping the estimation time within 10ms on a ×86-64 workstation.

KEYWORDS:

Radiofrequency; ablation; cancer; control; depth; ensemble; learning; lesion; machine; monitoring; tumor

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