Tissue post-classification using the measured acoustic signals during 355 nm laser atherectomy procedures.

The current laser atherectomy technologies to treat patients with challenging (to-cross) total chronic occlusions with a step-by-step (SBS) approach (without leading guide wire), are lacking real-time signal monitoring of the ablated tissues, and carry the risk for vessel perforation. We present first time post-classification of ablated tissues using acoustic signals recorded by a microphone placed nearby during five atherectomy procedures using 355 nm solid-state Auryon laser device performed with an SBS approach, some with highly severe calcification. Using our machine-learning algorithm, the classification results of these ablation signals recordings from five patients showed 93.7% classification accuracy with arterial vs nonarterial wall material. While still very preliminary and requiring a larger study and thereafter as commercial device, the results of these first acoustic post-classification in SBS cases are very promising. This study implies, as a general statement, that online recording of the acoustic signals using a noncontact microphone, may potentially serve for an online classification of the ablated tissue in SBS cases. This technology could be used to confirm correct positioning in the vasculature, and by this, to potentially further reduce the risk of perforation using 355 nm laser atherectomy in such procedures.

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.