Learning Temperature Dynamics on Agar-Based Phantom Tissue Surface During Single Point CO\(_2\) Laser Exposure

摘要

This paper presents an approach to determine a model of superficial tissue temperature dynamics during continuous wave CO\(_2\) laser irradiation. The main contribution of this research is the use of real data to model the thermal state of targets being irradiated with surgical laser, using statistical learning methods. To the best of our knowledge this is the first time that these methods have been applied in this field. This work extends previous results which demonstrated, in simulation, the relevance and utility of machine learning methods in modeling the thermal state of tissue during laser surgical procedures. Here we use real data, captured with a thermal camera, to learn the function mapping from laser inputs to the corresponding changes in tissue temperature. Based on features observed in the real data, a new structure for the model as well as a different learning approach are proposed.