Photothermal therapy system and method based on adaptive neural network

Photothermal therapy is an emerging tumor treatment technology used for precise treatment of malignant tumors. This technology utilizes materials with high photothermal conversion efficiency to aggregate in tumor tissue, where external therapeutic light sources generate light energy and convert it into thermal energy, selectively killing cancer cells. Compared with traditional treatment methods such as surgery, chemotherapy, and radiotherapy, photothermal therapy has the advantages of non-invasive, targeted, minimal side effects, and repeatable treatment. At the same time, the treatment interval of photothermal therapy is shorter, which can further alleviate the treatment pain of patients. Photothermal therapy technology can also improve immune efficacy by combining synergistic effects with other therapies such as immunotherapy. Due to the lack of real-Time and accurate temperature measurement technology, it is difficult to adjust the treatment power in real time, leading to consequences such as overtreatment and seriously affecting the effectiveness of photothermal therapy. Therefore, a non-invasive temperature measurement and regulation method is needed. We propose a photothermal therapy system and method based on adaptive neural network, and validate the algorithm and system using an agar based phantom. The experimental results show that the method has good temperature measurement and control characteristics, with a temperature measurement accuracy of 0.76 °C, and achieves a temperature control accuracy of 0.52 °C within a 21 seconds adjustment time. It is expected to further improve the safety and stability of PTT.