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.

Optical Biosensors for the Diagnosis of COVID-19 and Other Viruses-A Review.

The sudden outbreak of the COVID-19 pandemic led to a huge concern globally because of the astounding increase in mortality rates worldwide. The medical imaging computed tomography technique, whole-genome sequencing, and electron microscopy are the methods generally used for the screening and identification of the SARS-CoV-2 virus. The main aim of this review is to emphasize the capabilities of various optical techniques to facilitate not only the timely and effective diagnosis of the virus but also to apply its potential toward therapy in the field of virology. This review paper categorizes the potential optical biosensors into the three main categories, spectroscopic-, nanomaterial-, and interferometry-based approaches, used for detecting various types of viruses, including SARS-CoV-2. Various classifications of spectroscopic techniques such as Raman spectroscopy, near-infrared spectroscopy, and fluorescence spectroscopy are discussed in the first part. The second aspect highlights advances related to nanomaterial-based optical biosensors, while the third part describes various optical interferometric biosensors used for the detection of viruses. The tremendous progress made by lab-on-a-chip technology in conjunction with smartphones for improving the point-of-care and portability features of the optical biosensors is also discussed. Finally, the review discusses the emergence of artificial intelligence and its applications in the field of bio-photonics and medical imaging for the diagnosis of COVID-19. The review concludes by providing insights into the future perspectives of optical techniques in the effective diagnosis of viruses.

Real-time closed-loop tissue-specific laser osteotomy using deep-learning-assisted optical coherence tomography.

This article presents a real-time noninvasive method for detecting bone and bone marrow in laser osteotomy. This is the first optical coherence tomography (OCT) implementation as an online feedback system for laser osteotomy. A deep-learning model has been trained to identify tissue types during laser ablation with a test accuracy of 96.28 %. For the hole ablation experiments, the average maximum depth of perforation and volume loss was 0.216 mm and 0.077 mm3, respectively. The contactless nature of OCT with the reported performance shows that it is becoming more feasible to utilize it as a real-time feedback system for laser osteotomy.

Stochastic Differential Equations for Automatic Quality Control of Retinal Optical Coherence Tomography images.

Optical coherence tomography is widely used to provide high resolution images from retina. During data acquisition, several artifacts may be associated with OCT images which clearly remove information of retinal layers and degrade the quality of images. Manual assessment of the acquired OCT images is hard and time consuming. Therefore, an automatic quality control step is necessary to detect poor images for next decisions of eliminating them and even re-scanning. In this study, a novel automatic quality control methodology is proposed for early assessment of the OCT images quality by employing stochastic differential equations (SDE). In this method α-stable nature of OCT images is represented by applying a fractional Laplacian filter and parameters of the obtained α-stable are fed to an SVM to automatically detect high quality vs poor quality images. The simulation results on a large dataset of normal and abnormal OCT images show that proposed method has outstanding performance in detection of poor vs high quality images. The methodology is applicable to the image quality assessment of other OCT scanning devices as well. Clinical Relevance- Automatic quality control assessment of retinal OCT images provides reliable data for diagnosis of retinal and systematic diseases in clinical applications.

Mixture of Symmetric Stable Distributions for Macular Pathology Detection in Optical Coherence Tomography Scans.

Optical coherence tomography (OCT) is widely used to detect retinal disorders. In this study a new methodology is proposed for automatic detection of macular pathologies in the OCT images. Our approach is based on modeling the normal and abnormal OCT images with α-stable mixture model represented by stochastic differential equations (SDE). Parameters of the model are used to detect abnormal OCT images. The α-stable mixture model is created after applying a fractional Laplacian operator to the image and Expectation-Maximization (EM) algorithm is applied to estimate its parameters. The classification of an OCT image as normal or abnormal would be done by training SVM classifier based on estimated parameters of the mixture model. This method is examined for macular abnormality detection such as AMD, DME, and MH and achieve maximum accuracy of 97.8%. Clinical Relevance - This study establishes automatic method for anomaly detection on OCT images and provides fast and accurate OCT interpretation in clinical application.

Deep-Learning-Based Fast Optical Coherence Tomography (OCT) Image Denoising for Smart Laser Osteotomy.

Laser osteotomy promises precise cutting and minor bone tissue damage. We proposed Optical Coherence Tomography (OCT) to monitor the ablation process toward our smart laser osteotomy approach. The OCT image is helpful to identify tissue type and provide feedback for the ablation laser to avoid critical tissues such as bone marrow and nerve. Furthermore, in the implementation, the tissue classifier's accuracy is dependent on the quality of the OCT image. Therefore, image denoising plays an important role in having an accurate feedback system. A common OCT image denoising technique is the frame-averaging method. Inherent to this method is the need for multiple images, i.e., the more images used, the better the resulting image quality. However, this approach comes at the price of increased acquisition time and sensitivity to motion artifacts. To overcome these limitations, we applied a deep-learning denoising method capable of imitating the frame-averaging method. The resulting image had a similar image quality to the frame-averaging and was better than the classical digital filtering methods. We also evaluated if this method affects the tissue classifier model's accuracy that will provide feedback to the ablation laser. We found that image denoising significantly increased the accuracy of the tissue classifier. Furthermore, we observed that the classifier trained using the deep learning denoised images achieved similar accuracy to the classifier trained using frame-averaged images. The results suggest the possibility of using the deep learning method as a pre-processing step for real-time tissue classification in smart 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.

Long-range optical coherence tomography with extended depth-of-focus: a visual feedback system for smart laser osteotomy.

This work presents a long-range and extended depth-of-focus optical coherence tomography (OCT) system using a Bessel-like beam (BLB) as a visual feedback system during laser osteotomy. We used a swept-source OCT system (λ c = 1310 nm) with an imaging range of 26.2 mm in the air, integrated with a high energy microsecond Er:YAG laser operating at 2.94 µm. We demonstrated that the self-healing characteristics of the BLB could reduce the imaging artifacts that may arise during real-time monitoring of laser ablation. Furthermore, the feasibility of using long-range OCT to monitor a deep laser-induced incision is demonstrated.

Modeling of Retinal Optical Coherence Tomography Based on Stochastic Differential Equations: Application to Denoising.

In this paper a statistical modeling, based on stochastic differential equations (SDEs), is proposed for retinal Optical Coherence Tomography (OCT) images. In this method, pixel intensities of image are considered as discrete realizations of a Levy stable process. This process has independent increments and can be expressed as response of SDE to a white symmetric alpha stable (s [Formula: see text]) noise. Based on this assumption, applying appropriate differential operator makes intensities statistically independent. Mentioned white stable noise can be regenerated by applying fractional Laplacian operator to image intensities. In this way, we modeled OCT images as s [Formula: see text] distribution. We applied fractional Laplacian operator to image and fitted s [Formula: see text] to its histogram. Statistical tests were used to evaluate goodness of fit of stable distribution and its heavy tailed and stability characteristics. We used modeled s [Formula: see text] distribution as prior information in maximum a posteriori (MAP) estimator in order to reduce the speckle noise of OCT images. Such a statistically independent prior distribution simplified denoising optimization problem to a regularization algorithm with an adjustable shrinkage operator for each image. Alternating Direction Method of Multipliers (ADMM) algorithm was utilized to solve the denoising problem. We presented visual and quantitative evaluation results of the performance of this modeling and denoising methods for normal and abnormal images. Applying parameters of model in classification task as well as indicating effect of denoising in layer segmentation improvement illustrates that the proposed method describes OCT data more accurately than other models that do not remove statistical dependencies between pixel intensities.

Highly flexible fiber delivery of a high peak power nanosecond Nd:YAG laser beam for flexiscopic applications.

Minimally invasive laser surgeries that require the use of a flexible endoscope (flexiscope) could benefit from high-energy nanosecond laser pulses delivered through fibers for real-time tissue characterization and phenotyping. The damage threshold of the fiber's glass material limits the maximum amount of deliverable peak power. To transmit high-energy pulses without damaging the fiber material, large-diameter fibers are typically used, leading to a limited bending radius. Moreover, in a large-core fiber, self-focusing can damage the fiber even if the tip remains intact. In this work, we tested a fused-end fiber bundle combined with a beam shaper capable of delivering more than 20 MW (>100 mJ/5 ns). The fiber bundle was tested over more than eight hours of operation, with different bending radiuses down to 15 mm. The results demonstrate, to the best of our knowledge, the highest peak power delivered through a flexible fiber, for a frequency-doubled Q-switched Nd:YAG laser.

Machine learning monitoring for laser osteotomy.

This work proposes a new online monitoring method for an assistance during laser osteotomy. The method allows differentiating the type of ablated tissue and the applied dose of laser energy. The setup analyzes the laser-induced acoustic emission, detected by an airborne microphone sensor. The analysis of the acoustic signals is carried out using a machine learning algorithm that is pre-trained in a supervised manner. The efficiency of the method is experimentally evaluated with several types of tissues, which are: skin, fat, muscle, and bone. Several cutting-edge machine learning frameworks are tested for the comparison with the resulting classification accuracy in the range of 84-99%. It is shown that the datasets for the training of the machine learning algorithms are easy to collect in real-life conditions. In the future, this method could assist the doctors during laser osteotomy, minimizing the damage of the nearby healthy tissues and provide cleaner pathologic tissue removal.

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.

Combined Nd:YAG and Er:YAG lasers for real-time closed-loop tissue-specific laser osteotomy.

A novel real-time and non-destructive method for differentiating soft from hard tissue in laser osteotomy has been introduced and tested in a closed-loop fashion. Two laser beams were combined: a low energy frequency-doubled nanosecond Nd:YAG for detecting the type of tissue, and a high energy microsecond Er:YAG for ablating bone. The working principle is based on adjusting the energy of the Nd:YAG laser until it is low enough to create a microplasma in the hard tissue only (different energies are required to create plasma in different tissue types). Analyzing the light emitted from the generated microplasma enables real-time feedback to a shutter that prevents the Er:YAG laser from ablating the soft tissue.

Optimizing deep bone ablation by means of a microsecond Er:YAG laser and a novel water microjet irrigation system.

The microsecond Er:YAG pulsed laser with a wavelength of λ = 2.94 µm has been widely used in the medical field, particularly for ablating dental tissues. Since bone and dental tissues have similar compositions, consisting of mineralized and rigid structures, the Er:YAG laser represents a promising tool for laserosteotomy applications. In this study, we explored the use of the Er:YAG laser for deep bone ablation, in an attempt to optimize its performance and identify its limitations. Tissue irrigation and the laser settings were optimized independently. We propose an automated irrigation feedback system capable of recognizing the temperature of the tissue and delivering water accordingly. The irrigation system used consists of a thin 50 µm diameter water jet. The water jet was able to penetrate deep into the crater during ablation, with a laminar flow length of 15 cm, ensuring the irrigation of deeper layers unreachable by conventional spray systems. Once the irrigation was optimized, ablation was considered independently of the irrigation water. In this way, we could better understand and adjust the laser parameters to suit our needs. We obtained line cuts as deep as 21 mm without causing any visible thermal damage to the surrounding tissue. The automated experimental setup proposed here has the potential to support deeper and faster ablation in laserosteotomy applications.

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.

Characterization of Ablated Bone and Muscle for Long-Pulsed Laser Ablation in Dry and Wet Conditions.

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-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.