Abstract
The potential of Physics-Informed Neural Networks (PINNs) in addressing intricate real-world challenges exceeds the capabilities of traditional deep learning methods by merging data-driven and physics-driven approaches. PINNs offer advantages in handling computationally and temporally demanding problems that were conventionally approached using techniques like Finite Difference Method (FDM). This study focuses on analyzing heat transfer within cardiac tissue to deepen our understanding of the mechanism from ablation catheters or implants to surrounding blood and tissue layers. Accurately estimating temperature increases is crucial for designing reliable catheters and mitigating overheating risks. By defining the geometry and boundary conditions to simulate catheter-induced heating, we compare the results with state-of-the-art solutions such as FDM. PINN models demonstrate high accuracy, validated against FDM, with notable benefits including reduced reliance on handcrafted features, adaptability to complex domains, and efficient training and inference. These results can guide future investigations of the behavior of cardiac tissue under varied conditions.
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Acknowledgements
This research is funded by the project that has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101080905 (STRATIFYHF). This research is also supported by Ministry of Science, Technological Development and Innovation of the Republic of Serbia, contract numbers [451–03-47/2023–01/200107 (Faculty of Engineering, University of Kragujevac) and 451–03-47/2023–01/200378 (Institute for Information Technologies, University of Kragujevac)].
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Geroski, T., Pavić, O., Dašić, L., Filipović, N. (2024). Bridging the Gap: Physics-Driven Deep Learning for Heat Transfer Model of the Heart Tissue. In: Trajanović, M., Filipović, N., Zdravković, M. (eds) Disruptive Information Technologies for a Smart Society. ICIST 2024. Lecture Notes in Networks and Systems, vol 860. Springer, Cham. https://doi.org/10.1007/978-3-031-71419-1_14
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