Smart laser technologies are desired that can accurately cut and characterize tissues, such as bone and muscle, with minimal thermal damage and fast healing. Using a long-pulsed laser with a 0.5–10 ms pulse width at a wavelength of 1.07 µm, we investigated the optimum laser parameters for producing craters with minimal thermal damage under both wet and dry conditions. In different tissues (bone and muscle), we analyzed craters of various morphologies, depths, and volumes. We used a two-way Analysis of Variance (ANOVA) test to investigate whether there are significant differences in the ablation efficiency in wet versus dry conditions at each level of the pulse energy. We found that bone and muscle tissue ablated under wet conditions produced fewer cracks and less thermal damage around the craters than under dry conditions. In contrast to muscle, the ablation efficiency of bone under wet conditions was not higher than under dry conditions. Tissue differentiation was carried out based on measured acoustic waves. A Principal Component Analysis of the measured acoustic waves and Mahalanobis distances were used to differentiate bone and muscle under wet conditions. Bone and muscle ablated in wet conditions demonstrated a classification error of less than 6.66% and 3.33%, when measured by a microphone and a fiber Bragg grating, respectively.

Laser osteotomy offers a way to make precise and less traumatic cuts smaller than conventional mechanical bone surgery tools. To fully exploit the advantages of laser osteotomy over conventional osteotomy, real-time feedback to differentiate the hard bone from the surrounding soft tissues is desired. In this study, we differentiated various tissue types-hard and soft bone, fat, muscle, and skin tissues from five proximal and distal fresh porcine femurs-based on cutting sounds. For laser ablation, an Nd:YAG laser was used to create ten craters on the surface of each proximal and distal femurs. For sound recording, the probing beam of a Mach-Zehnder interferometer was placed 5 cm away from each ablation site. For offline tissue differentiation, we investigated the measurements by looking at the amplitude frequency band between 0.83 and 1.25 MHz, which provides the least average classification error. Then, we used principal component analysis to reduce the dimensionality and the 95% confidence ellipsoid (Mahalanobis distance) method to differentiate between tissues based on the acoustic shock wave. A set of 14 400 data points, measured from ten craters in four proximal and distal femurs, was used as "training data," while a set of 3600 data points from ten craters in the remaining proximal and distal femurs was considered as "testing data." As is seen in the confusion matrix, the experimental-based scores of hard and soft bones, fat, muscles, and skin yielded average classification errors (with leave-one-out cross validation) of 0.11%, 57.69%, 0.06%, 0.14%, and 2.92%, respectively. The results of this study demonstrate a promising technique for differentiating tissues during laser osteotomy.

A vector network analyser is used to study the electrical properties of multi‐walled carbon nanotube (MWCNT) thin films deposited on a fused quartz substrate in the sub‐terahertz (THz) frequency ranges of 220–325 GHz (WR3.4) and 325–500 GHz (WR2.2). The experiment is performed in free space. The complex permittivity of the MWCNT thin films is extracted using the Nicholson‐Ross‐Weir method. The refractive index and conductivity are then determined from the extracted permittivity. The method is validated by comparison with values obtained using THz time‐domain spectroscopy.

As laser osteotomy offers precise and small cuts with less trauma compared to conventional mechanical bone surgery. To fully exploit the advantages of laserosteotomes over conventional osteotomes, real-time feedback to differentiate hard bone from surrounding soft tissues is desired. In this study, we differentiated various tissue types - hard bone, fat and muscle tissue from an upper and lower fresh porcine thigh - based on cutting sounds. For laser ablation, an Nd:YAG laser was used to create ten craters on the surface of each upper and lower thigh. For sound recording, the probing beam of a Mach-Zehnder interferometer was placed 5cm away from each ablation site. For offline tissue differentiation, we investigated the measurements by looking at the amplitude of the spectrum. Then, we used Principle Component Analysis (PCA) to reduce the dimensionally and the 95% confidence ellipsoid (Mahalanobis distance) method to differentiate between tissues based on the acoustic shock wave. A set of 2520 data points, measured from the first seven craters of the upper and lower thigh, was used as `training data' a set of 1080 data points from the last three craters was considered as `testing data'. As seen in the confusion matrix, the experimental data from hard bone, fat and muscle yielded error rates of 0.46%, 0.19% and 1.30%, respectively. Preliminary results of this study demonstrate a promising technique for differentiating bone, fat and muscle tissues during laser surgery.

Today’s mechanical tools for bone cutting (osteotomy) lead to mechanical trauma that prolong the healing process. Medical device manufacturers continuously strive to improve their tools to minimize such trauma. One example of such a new tool and procedure is minimally invasive surgery with laser as the cutting element. This setup allows for tissue ablation using laser light instead of mechanical tools, which reduces the post-surgery healing time. During surgery, a reliable feedback system is crucial to avoid collateral damage to the surrounding tissues. Therefore, we propose a tissue classification method that analyzes the acoustic waves produced during laser ablation and show its applicability in an ex-vivo experiment. The ablation process with a microsecond pulsed Erbium-doped Yttrium Aluminium Garnet (Er:YAG) laser produces acoustic waves that we captured with an air-coupled transducer. Consequently, we used these captured waves to classify five porcine tissue types: hard bone, soft bone, muscle, fat, and skin tissue. For automated tissue classification of the measured acoustic waves, we propose three Neural Network (NN) approaches: A Fully-connected Neural Network (FcNN), a one-dimensional Convolutional Neural Network (CNN), and a Recurrent Neural Network (RNN). The time- and the frequency-dependent parts of the measured waves’ pressure variation were used as separate inputs to train and validate the designed NNs. In a final step, we used Grad-CAM to find the frequencies’ activation map and conclude that the low frequencies are the most important ones for this classification task. In our experiments, we achieved an accuracy of 100% for the five tissue types for all the proposed NNs. We tested the different classifiers for their robustness and concluded that using frequency-dependent data together with a FcNN is the most robust approach.