Early and Late Fusion Machine Learning on Multi-Frequency Electrical Impedance Data to Improve Radiofrequency Ablation Monitoring
Early and Late Fusion Machine Learning on Multi-Frequency Electrical Impedance Data to Improve Radiofrequency Ablation Monitoring
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DOI：
10.1109/jbhi.2019.2952922
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发表时间：
2020-08-01
影响因子：
7.7
通讯作者：
Sahakian, Alan V.
中科院分区：
文献类型：
--
作者：
Besler, Emre;Wang, Yearnchee Curtis;Sahakian, Alan V.
射频消融（RFA）是一种流行的肿瘤治疗方式。然而，在多种组织类型内廉价实时监测RFA仍然是一个正在进行的研究课题。本研究的目的是通过包括非线性机器学习（ML）模型的数据融合方案，在实时RFA深度估计中利用多频电阻抗数据。多频组织复电阻抗测量用于向数据融合方案提供输入数据。我们的研究结果表明，融合方案显着降低残差的传播和深度估计的残差的平均值。因此，数据融合可以是用于改进用于RFA的基于ML的监测的性能的重要工具。
Radiofrequency ablation (RFA) is a popular modality for tumor treatment. However, inexpensive real-time monitoring of RFA within multiple tissue types is still an ongoing research topic. The objective of this study is to utilize multi-frequency electrical impedance data within real-time RFA depth estimation through data fusion schemes that include non-linear machine learning (ML) models. Multi-frequency tissue complex electrical impedance measurements are used to provide input data to the data fusion schemes. Our results show that the fusion schemes significantly decrease both the spread of residuals and the mean of the residuals for depth estimation. Thus, data fusion can be a significant tool for use in improving the performance of ML-based monitoring for RFA.