Real-time monitoring of incision profile during laser surgery using shock wave detection

Biochemical implications of robotic surgery: a new frontier in the operating room

Robotic surgery represents a milestone in surgical procedures, offering advantages such as less invasive methods, elimination of tremors, scaled motion, and 3D visualization. This in-depth analysis explores the complex biochemical effects of robotic methods. The use of pneumoperitoneum and steep Trendelenburg positioning can decrease pulmonary compliance and splanchnic perfusion while increasing hypercarbia. However, robotic surgery reduces surgical stress and inflammation by minimizing tissue trauma. This contributes to faster recovery but may limit immune function. Robotic procedures also limit ischemia-reperfusion injury and oxidative damage compared to open surgery. They also help preserve native antioxidant defenses and coagulation. In a clinical setting, robotic procedures reduce blood loss, pain, complications, and length of stay compared to traditional procedures. However, risks remain, including device failure, the need for conversion to open surgery and increased costs. On the oncology side, there is still debate about margins, recurrence, and long-term survival. The advent of advanced technologies, such as intraoperative biosensors, localized drug delivery systems, and the incorporation of artificial intelligence, may further improve the efficiency of robotic surgery. However, ethical dilemmas regarding patient consent, privacy, access, and regulation of this disruptive innovation need to be addressed. Overall, this review sheds light on the complex biochemical implications of robotic surgery and highlights areas that require additional mechanistic investigation. It presents a comprehensive approach to responsibly maximize the potential of robotic surgery to improve patient outcomes, integrating technical skill with careful consideration of physiological and ethical issues.

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.

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.

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.

Modern trends in biophotonics for clinical diagnosis and therapy to solve unmet clinical needs

This contribution covers recent original research papers in the biophotonics field. The content is organized into main techniques such as multiphoton microscopy, Raman spectroscopy, infrared spectroscopy, optical coherence tomography and photoacoustic tomography, and their applications in the context of fluid, cell, tissue and skin diagnostics. Special attention is paid to vascular and blood flow diagnostics, photothermal and photodynamic therapy, tissue therapy, cell characterization, and biosensors for biomarker detection.

Noncontact monitoring of incision depth in laser surgery with air-coupled ultrasound transducers

Lack of haptic feedback during laser surgery makes it difficult to control the incision depth, leading to high risk of undesired tissue damage. Here, we present a new feedback sensing method that accomplishes noncontact real-time monitoring of laser ablation procedures by detecting shock waves emanating from the ablation spot with air-coupled transducers. Experiments in soft and hard tissue samples attained high reproducibility in real-time depth estimation of the laser-induced cuts. The advantages derived from the noncontact nature of the suggested monitoring approach are expected to advance the general applicability of laser-based surgeries.

Online estimation of laser incision depth for transoral microsurgery: approach and preliminary evaluation

BACKGROUND

The use of lasers in transoral surgery enables precise tissue incision with minimal adverse effects on surrounding structures. Nonetheless, the lack of haptic feedback during laser cutting impairs the surgeon's perception of the incision depth, potentially leading to undesired tissue damage.

METHODS

This paper presents a novel approach, based on statistical regression analysis, to estimate the laser incision depth in soft tissue. User trials were conducted in a laser surgery set-up, to verify the effectiveness of online estimation of incision depth in supporting precise tissue cutting.

RESULTS

The estimation accuracy was verified on ex vivo muscle tissue, revealing a root mean squared error (RMSE) of 0.1 mm for depths ranging up to 1.4 mm. Online estimation of depth has the potential to significantly improve the incision control of users.

CONCLUSIONS

The proposed approach was successful in producing estimations of laser cutting depth in ex vivo muscle tissue. Further investigation is required to validate this approach on other types of tissue. Providing depth estimation during laser cutting allows users to perform more precise incisions.