Simulation of Temperature Distribution in Biological Tissues Using Physics-Informed Neural Networks

The simulation in this chapter sheds light on the complicated thermal behavior of tissues, such as their thermal conductivity, blood perfusion rate, and thermal diffusivity. To streamline the analysis, metabolic heat generation was assumed to be zero in both tissues. The distribution of temperature in biological tissues offers several significant benefits across various fields including healthcare and medical research. The goal is to demonstrate the prospective synergy between physics-based knowledge and machine learning approaches, which contributes to improved understanding of temperature distribution in biological tissues and its implications for a variety of healthcare applications. Additionally the use of the Pennes equation which describes how heat is generated within tissues due to metabolic activities transferred through thermal conduction, and dissipated by blood perfusion was used to model the simulation.