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High-Frequency Irreversible Electroporation: Optimum Parameter Prediction via Machine-Learning

TitoloHigh-Frequency Irreversible Electroporation: Optimum Parameter Prediction via Machine-Learning
Tipo di pubblicazioneArticolo su Rivista peer-reviewed
Anno di Pubblicazione2024
AutoriDe Cillis, A., Merla Caterina, Monti G., Tarricone L., and Zappatore M.
RivistaIEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology
Type of ArticleArticle

The adoption of high-frequency irreversible electroporation in various medical treatments is becoming increasingly prevalent. There is currently a special focus on its applications in oncology, offering new perspectives in terms of treatable tumor types and treatment effectiveness. A multitude of parameters can influence the efficiency and effectiveness of high-frequency irreversible electroporation procedures, with the selection of suitable electrodes and possible prediction of ablated area as interesting examples. In this paper, we demonstrate that machine-learning strategies, specifically neural networks, provide an appropriate approach for optimizing the choice of some electrode characteristics, and predicting the ablation area, this being quite useful in high-frequency electroporation applications in oncology. This possibility, in turn, may lead to superior results in high-frequency irreversible electroporation, and to a significant reduction of the time required for achieving them. IEEE


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Citation KeyDe Cillis20241