High-Frequency Irreversible Electroporation: Optimum Parameter Prediction via Machine-Learning
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 pa...
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| Veröffentlicht in: | IEEE journal of electromagnetics, RF and microwaves in medicine and biology RF and microwaves in medicine and biology, 2024-09, Vol.8 (3), p.220-228 |
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| container_title | IEEE journal of electromagnetics, RF and microwaves in medicine and biology |
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| creator | De Cillis, A. Merla, C. Monti, G. Tarricone, L. Zappatore, M. |
| description | 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. |
| doi_str_mv | 10.1109/JERM.2024.3378573 |
| format | Article |
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| subjects | Ablation ablation area artificial neural network Artificial neural networks Effectiveness Electrodes Electromagnetics Electroporation High frequency irreversible electroporation Machine learning Medical treatment Neural networks Oncology Optimization Parameter estimation Parameters Prediction methods Predictions Protocols Reviews Tumors |
| title | High-Frequency Irreversible Electroporation: Optimum Parameter Prediction via Machine-Learning |
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