Physics-Informed Neural Networks for Nuclear Magnetic Resonance-Guided Clinical Hyperthermia

This chapter addresses a critical need for improved precision in the application of hyperthermia for clinical purposes. The developed approach advances our understanding of the physical mechanisms behind medical interventions and opens the door to more effective and personalized clinical hyperthermia, which could lead to improved patient outcomes and advancements in medical management. The goal is to design and implement a Physics-Informed Neural Network (PINN) to control clinical hyperthermia procedures based on Nuclear Magnetic Resonance (NMR). These involve (i) acquiring a comprehensive understanding of the relevant physical principles underlying clinical hyperthermia and ascertain the most effective means of integrating them into the neural network model; (ii) developing a robust neural network architecture and implement it to effectively integrate physics-based knowledge while maintaining the adaptability and learning capability of neural networks; (iii) creating a model that uses NMR data to guide clinical hyperthermia procedures in real time, enabling precise targeting and temperature monitoring of the treatment area; (iv) verifying the developed model in details against accepted practices and assess its effectiveness relative to traditional methods. This entails evaluating the accuracy, efficiency, and dependability of the Physics-Informed Neural Network in treating clinical hyperthermia.