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Real-Time Radiofrequency Ablation Lesion Depth Estimation Using Multi-frequency Impedance With a Deep Neural Network

Emre Besler, Yearnchee Curtis Wang, Alan V Sahakian

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    |November 2, 2019
    PubMed
    Summary

    Accurate radiofrequency ablation (RFA) lesion depth estimation was achieved using multi-frequency electrical impedance data. New statistical models significantly improved accuracy, enabling potential clinical translation.

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    Area of Science:

    • Medical Engineering
    • Biomedical Signal Processing
    • Machine Learning in Medicine

    Background:

    • Radiofrequency ablation (RFA) is a minimally invasive procedure requiring precise control of lesion depth.
    • Accurate real-time estimation of RFA lesion depth is crucial for effective treatment and minimizing collateral damage.
    • Current methods for RFA depth monitoring may lack the necessary precision or real-time capabilities for optimal clinical application.

    Purpose of the Study:

    • To design and optimize statistical models for estimating radiofrequency ablation (RFA) lesion depths with soft real-time performance.
    • To evaluate the efficacy of deep neural networks (NN) and tree-based ensembles (TEs) for RFA lesion depth prediction using multi-frequency electrical impedance data.
    • To enhance the accuracy of RFA lesion depth estimation by incorporating additional data features.

    Main Methods:

    • Utilized multi-frequency complex electrical impedance data from a low-cost embedded system.
    • Trained deep neural network (NN) and tree-based ensemble (TE) models for regression-based RFA lesion depth estimation.
    • Incorporated frequency sweep data, previous depth data, and previous RF power state data to improve model accuracy.

    Main Results:

    • The developed statistical models achieved significantly improved accuracy, with root mean square errors as low as 0.04 mm for TEs and 0.4 mm for NN.
    • Simulation ablation performance showed a mean difference of 0.5 ±0.2 mm for the NN method and 0.7 ±0.4 mm for the TE method against physical measurements.
    • Multi-frequency data was shown to significantly enhance the depth estimation performance of the statistical models.

    Conclusions:

    • Multi-frequency electrical impedance data substantially improves the accuracy of RFA lesion depth estimation models.
    • The proposed RFA lesion depth estimation methods achieve millimeter-resolution accuracy.
    • These methods demonstrate soft real-time performance on an embedded system, indicating potential for clinical RFA technology translation.