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

Abstract

Objectives: 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.

Materials and methods: 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.

Results: 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.

Conclusion: 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.

Keywords: acoustic shock signal; artificial network machine; laser ablation; principal component analysis; sciatic nerve tissue; tissue classification.

Figure 1

Tissue samples from a fresh porcine femur. Proximal femur: hard bone (A), soft bone (B), muscle (C), fat (D), skin (E), and nerve (F); distal femur: hard bone (G), soft bone (H), muscle (I), fat (J), skin (K), and sciatic nerve (L)

Figure 2

Illustration of the fiber‐coupled Fabry–Pérot etalon system (side view). The acoustic shock wave propagated is detected in the optical cavity as the refractive index in the air cavity changes

Figure 3

Flow chart of the signal processing methods for tissue classification

Figure 4

The architecture of our artificial neural network used to classify the first three principal component analysis scores of each acoustic shock wave from each tissue type

Figure 5

Comparison of the acoustic shock waves emitted from ablated hard muscle and fat tissues in the time and frequency domain

Figure 6

First three principal component (PC) scores from (A) the training data for hard and soft bone, muscle, fat, skin, and nerve (FR4 = 1.25–1.67 MHz), using the artificial neural network (ANN) models combined with the nanosecond (ns)‐Nd:YAG laser. First three PC scores from (B) the testing data for hard and soft bone, muscle, fat, skin, and nerve (FR4 = 1.25–1.67 MHz), using the ANN models combined with the ns‐Nd:YAG laser. The receiver operating characteristic curve (C) to multiclass using the ANN models combined with the ns‐Nd:YAG laser. Note that the curve of hard bone, skin, muscle, and nerve tissues versus other tissues overlaps

Figure A1

First three principal component (PC) scores from (A) the training data for hard and soft bone, muscle, fat, and skin (FR1 = 0–0.42 MHz). First three PC scores from (B) the testing data for hard and soft bone, muscle, fat, and skin (FR1 = 0–0.42 MHz). Receiver operating characteristic curve (C) to multiclass using the artificial neural network models combined with the ns‐Nd:YAG laser. Note that the curve of hard bone, skin, muscle, and nerve tissues versus other tissues overlaps (FR1 = 0–0.42 MHz)

Figure A2

First three principal component (PC) scores from (A) the training data for hard and soft bone, muscle, fat, and skin (FR2 = 0.42–0.83 MHz). First three PC scores from (B) the testing data for hard and soft bone, muscle, fat, and skin (FR2 = 0.42–0.83 MHz). Receiver operating characteristic curve (C) to multiclass using the artificial neural network models combined with the ns‐Nd:YAG laser. Note that the curve of hard bone, skin, muscle, and nerve tissues versus other tissues overlaps (FR2 = 0.42–0.83 MHz)

Figure A3

First three principal component (PC) scores from (A) the training data for hard and soft bone, muscle, fat, and skin (FR3 = 0.83–1.25 MHz). First three PC scores from (B) the testing data for hard and soft bone, muscle, fat, and skin (FR3 = 0.83–1.25 MHz). Receiver operating characteristic curve (C) to multiclass using the artificial neural network models combined with the ns‐Nd:YAG laser. Note that the curve of hard bone, skin, muscle, and nerve tissues versus other tissues overlaps (FR3 = 0.83–1.25 MHz)

Figure A4

First three principal component (PC) scores from (A) the training data for hard bone, soft bone, muscle, fat, skin, and nerve (FR5 = 1.67–2.08 MHz). First three PC scores from (B) the testing data for hard bone, soft bone, muscle, fat, skin, and nerve (FR5 = 1.67–2.08 MHz). Receiver operating characteristic curve (C) to multiclass using the artificial neural network models combined with the ns‐Nd:YAG laser. Note that the curve of hard bone, skin, muscle, and nerve tissues versus other tissues overlaps (FR5 = 1.67–2.08 MHz)

Figure A5

First three principal component (PC) scores from (A) the training data for hard bone, soft bone, muscle, fat, skin, and nerve (FR6 = 2.08–2.50 MHz). First three PC scores from (B) the testing data for hard bone, soft bone, muscle, fat, skin, and nerve (FR6 = 2.08–2.50 MHz). Receiver operating characteristic curve (C) to multiclass using the artificial neural network models combined with the ns‐Nd:YAG laser. Note that the curve of hard bone, skin, muscle, and nerve tissues versus other tissues overlaps (FR6 = 2.08–2.50 MHz)