Purpose

This paper aims to reduce the cost of experiments required to test the efficiency of materials suitable for artificial tissue ablation by increasing efficiency and accurately forecasting heating properties.

Design/methodology/approach

A two-step numerical analysis is used to develop and simulate a bioheat model using improved finite element method and deep learning algorithms, systematically regulating temperature distributions within the hydrogel artificial tissue during radiofrequency ablation (RFA). The model connects supervised learning and finite element analysis data to optimize electrode configurations, ensuring precise heat application while protecting surrounding hydrogel integrity.

Findings

The model accurately predicts a range of thermal changes critical for optimizing RFA, thereby enhancing treatment precision and minimizing impact on surrounding hydrogel materials. This computational approach not only advances the understanding of thermal dynamics but also provides a robust framework for improving therapeutic outcomes.

Originality/value

A computational predictive bioheat model, incorporating deep learning to optimize electrode configurations and minimize collateral tissue damage, represents a pioneering approach in interventional research. This method offers efficient evaluation of thermal strategies with reduced computational overhead compared to traditional numerical methods.

中文翻译：

---

集成预测方法以支持有限元分析并探索传热复杂性

 目的

本文旨在通过提高效率和准确预测加热特性来降低测试适用于人工组织消融的材料效率所需的实验成本。

设计/方法/方法

使用改进的有限元方法和深度学习算法，使用两步数值分析来开发和模拟生物热模型，在射频消融 （RFA） 期间系统地调节水凝胶人工组织内的温度分布。该模型将监督学习和有限元分析数据连接起来，以优化电极配置，确保精确的加热应用，同时保护周围的水凝胶完整性。

 发现

该模型准确预测了对优化 RFA 至关重要的一系列热变化，从而提高了处理精度并最大限度地减少了对周围水凝胶材料的影响。这种计算方法不仅促进了对热动力学的理解，还为改善治疗结果提供了一个强大的框架。

 原创性/价值

计算预测生物热模型结合了深度学习来优化电极配置并最大限度地减少侧支组织损伤，代表了介入研究的开创性方法。与传统的数值方法相比，这种方法提供了高效的热策略评估，减少了计算开销。