Y. Curtis Wang

Department of Electrical and Computer Engineering, California State University, Los Angeles, CA 90032

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TBME, Featured Articles
Real-time Radiofrequency Ablation Lesion Depth Estimation Using Multi-Frequency Impedance with a Deep Neural Network and Tree-based Ensemble
A combination of different machine learning algorithms and a hardware setup that consists of an embedded system and a 3D-printed electrode device is used to monitor the progress of radiofrequency ablation depth on a perfused breast tissue model. The device at the center of the tissue model both applied the alternating current and collected the tissue impedance data at multiple frequencies, which is fed into tree-based ensemble (TE) models and a deep neural network (DNN). Their predictions showed a mean difference against physical measurements of 0.5 mm for the DNN and 0.7 mm for the TEs... Read more