Optimization of an artificial neural network to estimate laser ablation efficiency

The use of fiber laser in surgery has many advantages due to its small size, efficiency, operation in continuous and pulse mode and easy coupling to fiber optics for various applications. Laser surgery requires very precise control on laser parameters, and imprecision can cause unacceptable damages during operation, particularly in neurosurgical applications. There have been many experimental studies looking to optimize laser parameters to control laser thermal therapy. In this study, we propose an artificial neural network method (ANN) to predict laser ablation damage as a function of laser parameters and temperature. The purpose of this study was to investigate the performance of artificial neural networks to predict the ablation efficiency of a 1940 nm thulium fiber laser on ovine brain tissue. Twelve experimental data were used to train an ANN model. Tissue type (cortical/subcortical), laser mode, laser power, laser energy, time, temperature and temperature change were used as inputs for the ANN and the ablation efficiency (ablated area/total thermally altered area) was the output of the model. Four untrained data were used to validate the ANN prediction ability after finding correlation between the ANN inputs and output. Experimental and predicted data were compared to find the accuracy of the model. Moreover, optimum laser mode (continuous/pulse) selection was also studied. Five different machine learning methods were used for laser mode selection, and the results were compared. Our results showed that prediction for the ablation efficiency of an ANN is lower than 15% and 87.5% classification accuracy was obtained for optimum laser mode selection. (paper)