Multimodal feedback systems for smart laser osteotomy: Depth control and tissue differentiation.

OBJECTIVES: The study aimed to improve the safety and accuracy of laser osteotomy (bone surgery) by integrating optical feedback systems with an Er:YAG laser. Optical feedback consists of a real-time visual feedback system that monitors and controls the depth of laser-induced cuts and a tissue sensor differentiating tissue types based on their chemical composition. The developed multimodal feedback systems demonstrated the potential to enhance the safety and accuracy of laser surgery. MATERIALS AND METHODS: The proposed method utilizes a laser-induced breakdown spectroscopy (LIBS) system and long-range Bessel-like beam optical coherence tomography (OCT) for tissue-specific laser surgery. The LIBS system detects tissue types by analyzing the plasma generated on the tissue by a nanosecond Nd:YAG laser, while OCT provides real-time monitoring and control of the laser-induced cut depth. The OCT system operates at a wavelength of 1288 ± 30 nm and has an A-scan rate of 104.17 kHz, enabling accurate depth control. Optical shutters are used to facilitate the integration of these multimodal feedback systems. RESULTS: The proposed system was tested on five specimens of pig femur bone to evaluate its functionality. Tissue differentiation and visual depth feedback were used to achieve high precision both on the surface and in-depth. The results showed successful real-time tissue differentiation and visualization without any visible thermal damage or carbonization. The accuracy of the tissue differentiation was evaluated, with a mean absolute error of 330.4 µm and a standard deviation of ±248.9 µm, indicating that bone ablation was typically stopped before reaching the bone marrow. The depth control of the laser cut had a mean accuracy of 65.9 µm with a standard deviation of ±45 µm, demonstrating the system's ability to achieve the pre-planned cutting depth. CONCLUSION: The integrated approach of combining an ablative laser, visual feedback (OCT), and tissue sensor (LIBS) has significant potential for enhancing minimally invasive surgery and warrants further investigation and development.

Towards phase-sensitive optical coherence tomography in smart laser osteotomy: temperature feedback.

Thermal effects during bone surgery pose a common challenge, whether using mechanical tools or lasers. An irrigation system is a standard solution to cool the tissue and reduce collateral thermal damage. In bone surgery using Er:YAG laser, insufficient irrigation raises the risk of thermal damage, while excessive water lowers ablation efficiency. This study investigated the potential of optical coherence tomography to provide feedback by relating the temperature rise with the photo-thermal expansion of the tissue. A phase-sensitive optical coherence tomography system (central wavelength of λ=1.288 µm, a bandwidth of 60.9 nm and a sweep rate of 104.17 kHz) was integrated with an Er:YAG laser using a custom-made dichromatic mirror. Phase calibration was performed by monitoring the temperature changes (thermal camera) and corresponding cumulative phase changes using the phase-sensitive optical coherence tomography system during laser ablation. In this experiment, we used an Er:YAG laser with 230 mJ per pulse at 10 Hz for ablation. Calibration coefficients were determined by fitting the temperature values to phase later and used to predict the temperature rise for subsequent laser ablations. Following the phase calibration step, we used the acquired values to predict the temperature rise of three different laser-induced cuts with the same parameters of the ablative laser. The average root-mean-square error for the three experiments was measured to be around 4 °C. In addition to single-point prediction, we evaluated this method's performance to predict the tissue's two-dimensional temperature rise during laser osteotomy. The findings suggest that the proposed principle could be used in the future to provide temperature feedback for minimally invasive laser osteotomy.

Fabrication and Characterization of a Flexible FBG-Based Shape Sensor Using Single-Mode Fibers.

Minimally invasive surgical procedures have become the preferable option, as the recovery period and the risk of infections are significantly lower than traditional surgeries. However, the main challenge in using flexible tools for minimal surgical interventions is the lack of precise feedback on their shape and tip position inside the patient's body. Shape sensors based on fiber Bragg gratings (FBGs) can provide accurate shape information depending on their design. One of the most common configurations in FBG-based shape sensors is to attach three single-mode optical fibers with arrays of FBGs in a triangular fashion around a substrate. Usually, the selected substrates dominate the bending stiffness of the sensor probe, as they have a larger diameter and show less flexibility compared to the optical fibers. Although sensors with this configuration can accurately estimate the shape, they cannot be implemented in flexible endoscopes where large deflections are expected. This paper investigates the shape sensor's performance when using a superelastic substrate with a small diameter instead of a substrate with dominating bending stiffness. A generalized model is also designed for characterizing this type of flexible FBG-based shape sensor. Moreover, we evaluated the sensor in single and multi-bend deformations using two shape reconstruction methods.

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

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.

Optical fibers for endoscopic high-power Er:YAG laserosteotomy.

SIGNIFICANCE: The highest absorption peaks of the main components of bone are in the mid-infrared region, making Er:YAG and CO2 lasers the most efficient lasers for cutting bone. Yet, studies of deep bone ablation in minimally invasive settings are very limited, as finding suitable materials for coupling high-power laser light with low attenuation beyond 2 µm is not trivial. AIM: The first aim of this study was to compare the performance of different optical fibers in terms of transmitting Er:YAG laser light with a 2.94-µm wavelength at high pulse energy close to 1 J. The second aim was to achieve deep bone ablation using the best-performing fiber, as determined by our experiments. APPROACH: In our study, various optical fibers with low attenuation (λ = 2.94 µm) were used to couple the Er:YAG laser. The fibers were made of germanium oxide, sapphire, zirconium fluoride, and hollow-core silica, respectively. We compared the fibers in terms of transmission efficiency, resistance to high Er:YAG laser energy, and bending flexibility. The best-performing fiber was used to achieve deep bone ablation in a minimally invasive setting. To do this, we adapted the optimal settings for free-space deep bone ablation with an Er:YAG laser found in a previous study. RESULTS: Three of the fibers endured energy per pulse as high as 820 mJ at a repetition rate of 10 Hz. The best-performing fiber, made of germanium oxide, provided higher transmission efficiency and greater bending flexibility than the other fibers. With an output energy of 370 mJ per pulse at 10 Hz repetition rate, we reached a cutting depth of 6.82 ± 0.99 mm in sheep bone. Histology image analysis was performed on the bone tissue adjacent to the laser ablation crater; the images did not show any structural damage. CONCLUSIONS: The findings suggest that our prototype could be used in future generations of endoscopic devices for minimally invasive laserosteotomy.

Machine Learning-Based Optoacoustic Tissue Classification Method for Laser Osteotomes Using an Air-Coupled Transducer.

BACKGROUND AND OBJECTIVES: Using lasers instead of mechanical tools for bone cutting holds many advantages, including functional cuts, contactless interaction, and faster wound healing. To fully exploit the benefits of lasers over conventional mechanical tools, a real-time feedback to classify tissue is proposed. STUDY DESIGN/MATERIALS AND METHODS: In this paper, we simultaneously classified five tissue types-hard and soft bone, muscle, fat, and skin from five proximal and distal fresh porcine femurs-based on the laser-induced acoustic shock waves (ASWs) generated. For laser ablation, a nanosecond frequency-doubled Nd:YAG laser source at 532 nm and a microsecond Er:YAG laser source at 2940 nm were used to create 10 craters on the surface of each proximal and distal femur. Depending on the application, the Nd:YAG or Er:YAG can be used for bone cutting. For ASW recording, an air-coupled transducer was placed 5 cm away from the ablated spot. For tissue classification, we analyzed the measured acoustics by looking at the amplitude-frequency band of 0.11-0.27 and 0.27-0.53 MHz, which provided the least average classification error for Er:YAG and Nd:YAG, respectively. For data reduction, we used the amplitude-frequency band as an input of the principal component analysis (PCA). On the basis of PCA scores, we compared the performance of the artificial neural network (ANN), the quadratic- and Gaussian-support vector machine (SVM) to classify tissue types. A set of 14,400 data points, measured from 10 craters in four proximal and distal femurs, was used as training data, while a set of 3,600 data points from 10 craters in the remaining proximal and distal femur was considered as testing data, for each laser. RESULTS: The ANN performed best for both lasers, with an average classification error for all tissues of 5.01 ± 5.06% and 9.12 ± 3.39%, using the Nd:YAG and Er:YAG lasers, respectively. Then, the Gaussian-SVM performed better than the quadratic SVM during the cutting with both lasers. The Gaussian-SVM yielded average classification errors of 15.17 ± 13.12% and 16.85 ± 7.59%, using the Nd:YAG and Er:YAG lasers, respectively. The worst performance was achieved with the quadratic-SVM with a classification error of 50.34 ± 35.04% and 69.96 ± 25.49%, using the Nd:YAG and Er:YAG lasers. CONCLUSION: We foresee using the ANN to differentiate tissues in real-time during laser osteotomy. Lasers Surg. Med. © 2020 Wiley Periodicals LLC.

Optoacoustic Tissue Differentiation Using a Mach-Zehnder Interferometer.

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.

Laser-induced breakdown spectroscopy as a potential tool for autocarbonization detection in laserosteotomy.

In laserosteotomy, it is vital to avoid thermal damage of the surrounding tissue, such as carbonization, since carbonization does not only deteriorate the ablation efficiency but also prolongs the healing process. The state-of-the-art method to avoid carbonization is irrigation systems; however, it is difficult to determine the desired flow rate of the air and cooling water based on previous experiments without online monitoring of the bone surface. Lack of such feedback during the ablation process can cause carbonization in case of a possible error in the irrigation system or slow down the cutting process when irrigating with too much cooling water. The aim of this paper is to examine laser-induced breakdown spectroscopy as a potential tool for autocarbonization detection in laserosteotomy. By monitoring the laser-driven plasma generated during nanosecond pulse ablation of porcine bone samples, carbonization is hypothesized to be detectable. For this, the collected spectra were analyzed based on variation of a specific pair of emission line ratios in both groups of samples: normal and carbonized bone. The results confirmed a high accuracy of over 95% in classifying normal and carbonized bone.

Advances in bone marrow stem cell therapy for retinal dysfunction.

The most common cause of untreatable vision loss is dysfunction of the retina. Conditions, such as age-related macular degeneration, diabetic retinopathy and glaucoma remain leading causes of untreatable blindness worldwide. Various stem cell approaches are being explored for treatment of retinal regeneration. The rationale for using bone marrow stem cells to treat retinal dysfunction is based on preclinical evidence showing that bone marrow stem cells can rescue degenerating and ischemic retina. These stem cells have primarily paracrine trophic effects although some cells can directly incorporate into damaged tissue. Since the paracrine trophic effects can have regenerative effects on multiple cells in the retina, the use of this cell therapy is not limited to a particular retinal condition. Autologous bone marrow-derived stem cells are being explored in early clinical trials as therapy for various retinal conditions. These bone marrow stem cells include mesenchymal stem cells, mononuclear cells and CD34+ cells. Autologous therapy requires no systemic immunosuppression or donor matching. Intravitreal delivery of CD34+ cells and mononuclear cells appears to be tolerated and is being explored since some of these cells can home into the damaged retina after intravitreal administration. The safety of intravitreal delivery of mesenchymal stem cells has not been well established. This review provides an update of the current evidence in support of the use of bone marrow stem cells as treatment for retinal dysfunction. The potential limitations and complications of using certain forms of bone marrow stem cells as therapy are discussed. Future directions of research include methods to optimize the therapeutic potential of these stem cells, non-cellular alternatives using extracellular vesicles, and in vivo high-resolution retinal imaging to detect cellular changes in the retina following cell therapy.

In vivo optical tissue differentiation by diffuse reflectance spectroscopy: preliminary results for tissue-specific laser surgery.

OBJECTIVES: Laser surgery requires feedback to avoid the accidental destruction of critically important tissues. It was the aim of the authors to identify different tissue types in vivo by diffuse reflectance spectroscopy to set the basis for tissue-specific control of laser surgery. METHODS: Tissue differentiation was performed on in vivo tissue of rats (skin, fat, muscle, and nerve) by diffuse reflectance spectroscopy between 350 and 650 nm. Data analysis was done using principal components analysis, followed by linear discriminant analysis (LDA). The differentiation performance was evaluated by receiver operating characteristic (ROC) analysis. RESULTS: ROC analysis showed a tissue differentiation of 100%, with a high sensitivity of more than 99%. Only the tissue pair skin/fat showed a reduced differentiation performance and specificity. CONCLUSION: The results show the general viability of in vivo optical tissue differentiation and create a basis for the further development of a control system for tissue-specific laser surgery.

Optical nerve detection by diffuse reflectance spectroscopy for feedback controlled oral and maxillofacial laser surgery.

BACKGROUND: Laser surgery lacks haptic feedback, which is accompanied by the risk of iatrogenic nerve damage. It was the aim of this study to investigate diffuse reflectance spectroscopy for tissue differentiation as the base of a feedback control system to enhance nerve preservation in oral and maxillofacial laser surgery. METHODS: Diffuse reflectance spectra of nerve tissue, salivary gland and bone (8640 spectra) of the mid-facial region of ex vivo domestic pigs were acquired in the wavelength range of 350-650 nm. Tissue differentiation was performed using principal component (PC) analysis followed by linear discriminant analysis (LDA). Specificity and sensitivity were calculated using receiver operating characteristic (ROC) analysis and the area under curve (AUC). RESULTS: Five PCs were found to be adequate for tissue differentiation with diffuse reflectance spectra using LDA. Nerve tissue could be differed from bone as well as from salivary gland with AUC results of greater than 88%, sensitivity of greater than 83% and specificity in excess of 78%. CONCLUSIONS: Diffuse reflectance spectroscopy is an adequate technique for nerve identification in the vicinity of bone and salivary gland. The results set the basis for a feedback system to prevent iatrogenic nerve damage when performing oral and maxillofacial laser surgery.

Diffuse reflectance spectroscopy for optical soft tissue differentiation as remote feedback control for tissue-specific laser surgery.

BACKGROUND AND OBJECTIVE: Laser surgery does not provide haptic feedback for operating layer-by-layer and thereby preserving vulnerable anatomical structures like nerve tissue or blood vessels. Diffuse reflectance spectra can facilitate remote optical tissue differentiation. It is the aim of the study to use this technique on soft tissue samples, to set a technological basis for a remote optical feedback system for tissue-specific laser surgery. MATERIALS AND METHODS: Diffuse reflectance spectra (wavelength range: 350-650 nm) of ex vivo types of soft tissue (a total of 10,800 spectra) of the midfacial region of domestic pigs were remotely measured under reduced environmental light conditions and analyzed in order to differentiate between skin, mucosa, muscle, subcutaneous fat, and nerve tissue. We performed a principal components (PC) analysis (PCA) to reduce the number of variables. Linear discriminant analysis (LDA) was utilized for classification. For the tissue differentiation, we calculated the specificity and sensitivity by receiver operating characteristic (ROC) analysis and the area under curve (AUC). RESULTS: Six PCs were found to be adequate for tissue differentiation with diffuse reflectance spectra using LDA. All of the types of soft tissue could be differentiated with high specificity and sensitivity. Only the tissue pairs nervous tissue/fatty tissue and nervous tissue/mucosa showed a decline of differentiation due to bio-structural similarity. However, both of these tissue pairs could still be differentiated with a specificity and sensitivity of more than 90%. CONCLUSIONS: Analyzing diffuse reflectance spectroscopy with PCA and LDA allows for remote differentiation of biological tissue. Considering the limitations of the ex vivo conditions, the obtained results are promising and set a basis for the further development of a feedback system for tissue-specific laser surgery.