Abstract

We report on a custom-made, fiber-coupled Fabry-Pérot etalon sensor to measure the acoustic shock waves (ASW) generated during laser ablation. A frequency-doubled Nd:YAG laser (532 nm) with a 5 ns pulse duration was used to produce craters on the surfaces of five different tissues —hard and soft bone, muscle, fat and skin— from five fresh porcine proximal and distal femur specimens. After collecting the ASW signals generated during laser ablation, we split the Fourier spectrum of the measured ASW into six equal bands and used each as an input for principal component analysis (PCA). We used PCA to reduce the dimensionality of each band and fed the PCA scores to an Artificial Neural Network (ANN) for classification. The most accurate tissue differentiation occurred at a band of 1.67–2.08 MHz. In total 18000 data points were collected from the femur samples and split into training (10800), validation (3600, and testing (3600) data. From a confusion matrix and the receiver operating characteristic (ROC), we observed that the experimental-based scores of hard and soft bone, fat, muscle and skin yielded average classification accuracies (with leave-one-out cross-validation) of 100 %, 99.55 %, 88.89%, 99.33%, and 100%, respectively. The area under the ROC curve (AUC) was more than 98.61 %, for all tissue types. The proposed method has the potential to provide real-time feedback during laser osteotomy, to prevent the cutting of vital tissues.

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