高频不可逆电穿孔:通过机器学习进行最佳参数预测

IF 3 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
A. De Cillis;C. Merla;G. Monti;L. Tarricone;M. Zappatore
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引用次数: 0

摘要

在各种医学治疗中采用高频不可逆电穿孔技术正变得越来越普遍。目前,高频不可逆电穿孔在肿瘤学中的应用受到特别关注,这为可治疗的肿瘤类型和治疗效果提供了新的视角。许多参数都会影响高频不可逆电穿孔手术的效率和效果,选择合适的电极和预测消融面积就是有趣的例子。在本文中,我们展示了机器学习策略,特别是神经网络,为优化选择某些电极特性和预测消融面积提供了一种合适的方法,这在肿瘤学的高频电穿孔应用中非常有用。反过来,这种可能性可能会导致高频不可逆电穿孔取得更好的效果,并显著缩短实现这些效果所需的时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.
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来源期刊
CiteScore
5.80
自引率
9.40%
发文量
58
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