Integrating forecasting methods to support finite element analysis and explore heat transfer complexities

Abstract

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.