Abstract
This paper presents the simulation and learning of soft tissue temperature dynamics when exposed to laser radiation. Monte Carlo simulation is used to represent the photon distribution in the tissue while machine learning techniques are used to obtain the mapping from controllable laser inputs (power, pulse rate and exposure time) to the correspondent changes in temperature. This model is required to predict the effects of laser-tissue interaction during surgery, i.e., tissue incision depth and carbonization.
The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 – Challenge 2 – Cognitive Systems, Interaction, Robotics – under grant agreement μRALP N°288233.
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Fichera, L., Pardo, D., Mattos, L.S. (2013). Modeling Tissue Temperature Dynamics during Laser Exposure. In: Rojas, I., Joya, G., Cabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38682-4_12
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DOI: https://doi.org/10.1007/978-3-642-38682-4_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38681-7
Online ISBN: 978-3-642-38682-4
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