Toward optoacoustic sciatic nerve detection using an all-fiber interferometric-based sensor for endoscopic smart...

Laser surgery requires efficient tissue classification to reduce the probability of undesirable or unwanted tissue damage. This study aimed to investigate acoustic shock waves (ASWs) as a means of classifying sciatic nerve tissue.

In this study, we classified sciatic nerve tissue against other tissue types-hard bone, soft bone, fat, muscle, and skin extracted from two proximal and distal fresh porcine femurs-using the ASWs generated by a laser during ablation. A nanosecond frequency-doubled Nd:YAG laser at 532 nm was used to create 10 craters on each tissue type's surface. We used a fiber-coupled Fabry-Pérot sensor to measure the ASWs. The spectrum's amplitude from each ASW frequency band measured was used as input for principal component analysis (PCA). PCA was combined with an artificial neural network to classify the tissue types. A confusion matrix and receiver operating characteristic (ROC) analysis was used to calculate the accuracy of the testing-data-based scores from the sciatic nerve and the area under the ROC curve (AUC) with a 95% confidence-level interval.

Based on the confusion matrix and ROC analysis of the model's tissue classification results (leave-one-out cross-validation), nerve tissue could be classified with an average accuracy rate and AUC result of 95.78  ± 1.3% and 99.58  ± 0.6%, respectively.

This study demonstrates the potential of using ASWs for remote classification of nerve and other tissue types. The technique can serve as the basis of a feedback control system to detect and preserve sciatic nerves in endoscopic laser surgery.