In situ tissue classification during laser ablation using acoustic signals.

We suggest a novel method to classify the type of tissue that is being ablated, using the recorded acoustic sound waves during pulsed ultraviolet laser ablation. The motivation of the current research is tissue classification during vascular interventions, where the identification of the ablated tissue is vital. We classify the acoustic signatures using Mel-frequency cepstral coefficients (MFCCs) feature extraction with a Support Vector Machine (SVM) algorithm, and in addition, use a fully connected deep neural network (FC-DNN) algorithm. First, we classify three different liquids using our method as a preliminary proof of concept. Then, we classify ex vivo porcine aorta and bovine tendon tissues in the presence of saline. Finally, we classify ex vivo porcine aorta and bovine tendon tissues where the acoustic signals are recorded through chicken breast medium. The results for tissue classification in saline and through chicken breast both show high accuracy (>98%), based on tens of thousands of acoustic signals for each experiment. The experiments were conducted in a noisy and challenging setting that tries to imitate practical working conditions. The obtained results could pave the way towards practical tissue classification in various important medical procedures, achieving enhanced efficacy and safety.