Clinical Feasibility of Applying Immersive Virtual Reality during Robot-Assisted Gait Training for Individuals with Neurological Diseases: A Pilot Study.

BACKGROUND: Immersive virtual reality has the potential to motivate and challenge patients who need and want to relearn movements in the process of neurorehabilitation. OBJECTIVE: The aim of this study was to evaluate the feasibility and user acceptance of an innovative immersive virtual reality system (head-mounted display) used in combination with robot-assisted gait training in subjects suffering from neurological diseases. METHODS: Fifteen participants suffering from cerebrovascular accident or spinal cord injury completed a single session of immersive virtual reality using a head-mounted display during a Lokomat® gait session. Training parameters and safety indicators were collected, and acceptance was investigated among participants and therapists. RESULTS: The results suggest that an immersive virtual reality system is feasible in terms of safety and tolerance. Furthermore, the very positive overall acceptance of the system suggests that it has the potential to be included in a robot-assisted gait training session using Lokomat®. CONCLUSION: Overall, this study demonstrates that a fully immersive virtual reality system based on a head-mounted display is both feasible and well received by cerebrovascular accident and spinal cord injury patients and their therapists during robot-assisted gait training. This study suggests that such a virtual reality system could be a viable alternative to the screen-based training games currently used in neurorehabilitation. It may be especially suitable for enhancing patient motivation and adherence to training, particularly if the application is enjoyable and not mentally taxing.

Hardness changing tactile displays for simulating the feel of organic tissues.

Physical interaction with patients, for example conducted as part of a diagnostic examination or surgical procedure, provides clinicians with a wealth of information about their condition. Simulating this interaction is of great interest to researchers in both haptics and medical education, and the development of softness changing tactile interfaces is important in recreating the feel of different soft tissues. This paper presents designs for a variety of novel electromechanical and electromagnetic mechanisms for controlling particle jamming-based, hardness changing tactile displays, intended to allow medical trainees to experience these physical interactions in a range of simulation settings such as clinical skills teaching laboratories. Each design is then subjected to a battery of mechanical tests to evaluate its effectiveness compared to the state of the art, as well as their suitability for simulating the physical hardness of different types of soft tissues, previously characterised in established literature. These results demonstrate that all of the technologies presented are able to exhibit a measurable hardness change, with Shore hardness values between 3A and 57A achieved by the most effective constriction-based device. The electromechanical devices based on constriction and compression, and the state-of-the-art pneumatic device, were able to achieve hardness changes within a range that is useful for replicating the softness of organic tissue. The electromechanical and electromagnetic devices were also found to effect their full range of hardness change in less than a second, compared to several seconds for the state-of-the-art. These results show that the performance of softness changing tactile displays can be improved with the electromechanical actuation techniques proposed in this paper, and that such displays are able to replicate the physical characteristics of soft tissues and may therefore be of benefit in medical training and simulation scenarios.

6G in medical robotics: development of network allocation strategies for a telerobotic examination system.

PURPOSE: Healthcare systems around the world are increasingly facing severe challenges due to problems such as staff shortage, changing demographics and the reliance on an often strongly human-dependent environment. One approach aiming to address these issues is the development of new telemedicine applications. The currently researched network standard 6G promises to deliver many new features which could be beneficial to leverage the full potential of emerging telemedical solutions and overcome the limitations of current network standards. METHODS: We developed a telerobotic examination system with a distributed robot control infrastructure to investigate the benefits and challenges of distributed computing scenarios, such as fog computing, in medical applications. We investigate different software configurations for which we characterize the network traffic and computational loads and subsequently establish network allocation strategies for different types of modular application functions (MAFs). RESULTS: The results indicate a high variability in the usage profiles of these MAFs, both in terms of computational load and networking behavior, which in turn allows the development of allocation strategies for different types of MAFs according to their requirements. Furthermore, the results provide a strong basis for further exploration of distributed computing scenarios in medical robotics. CONCLUSION: This work lays the foundation for the development of medical robotic applications using 6G network architectures and distributed computing scenarios, such as fog computing. In the future, we plan to investigate the capability to dynamically shift MAFs within the network based on current situational demand, which could help to further optimize the performance of network-based medical applications and play a role in addressing the increasingly critical challenges in healthcare.

Evaluating the Status and Promising Potential of Robotic Spinal Surgery Systems.

The increasing frequency of cervical and lumbar spine disorders, driven by aging and evolving lifestyles, has led to a rise in spinal surgeries using pedicle screws. Robotic spinal surgery systems have emerged as a promising innovation, offering enhanced accuracy in screw placement and improved surgical outcomes. We focused on literature of this field from the past 5 years, and a comprehensive literature search was performed using PubMed and Google Scholar. Robotic spinal surgery systems have significantly impacted spinal procedures by improving pedicle screw placement accuracy and supporting various techniques. These systems facilitate personalized, minimally invasive, and low-radiation interventions, leading to greater precision, reduced patient risk, and decreased radiation exposure. Despite advantages, challenges such as high costs and a steep learning curve remain. Ongoing advancements are expected to further enhance these systems' role in spinal surgery.

Towards Autonomous Retinal Microsurgery Using RGB-D Images.

Retinal surgery is a challenging procedure requiring precise manipulation of the fragile retinal tissue, often at the scale of tens-of-micrometers. Its difficulty has motivated the development of robotic assistance platforms to enable precise motion, and more recently, novel sensors such as microscope integrated optical coherence tomography (OCT) for RGB-D view of the surgical workspace. The combination of these devices opens new possibilities for robotic automation of tasks such as subretinal injection (SI), a procedure that involves precise needle insertion into the retina for targeted drug delivery. Motivated by this opportunity, we develop a framework for autonomous needle navigation during SI. We develop a system which enables the surgeon to specify waypoint goals in the microscope and OCT views, and the system autonomously navigates the needle to the desired subretinal space in real-time. Our system integrates OCT and microscope images with convolutional neural networks (CNNs) to automatically segment the surgical tool and retinal tissue boundaries, and model predictive control that generates optimal trajectories that respect kinematic constraints to ensure patient safety. We validate our system by demonstrating 30 successful SI trials on pig eyes. Preliminary comparisons to a human operator in robot-assisted mode highlight the enhanced safety and performance of our system.

Advances of surgical robotics: image-guided classification and application.

Surgical robotics application in the field of minimally invasive surgery has developed rapidly and has been attracting increasingly more research attention in recent years. A common consensus has been reached that surgical procedures are to become less traumatic and with the implementation of more intelligence and higher autonomy, which is a serious challenge faced by the environmental sensing capabilities of robotic systems. One of the main sources of environmental information for robots are images, which are the basis of robot vision. In this review article, we divide clinical image into direct and indirect based on the object of information acquisition, and into continuous, intermittent continuous, and discontinuous according to the target-tracking frequency. The characteristics and applications of the existing surgical robots in each category are introduced based on these two dimensions. Our purpose in conducting this review was to analyze, summarize, and discuss the current evidence on the general rules on the application of image technologies for medical purposes. Our analysis gives insight and provides guidance conducive to the development of more advanced surgical robotics systems in the future.

Tissue palpation in endoscopy using EIT and soft actuators.

The integration of soft robots in medical procedures has significantly improved diagnostic and therapeutic interventions, addressing safety concerns and enhancing surgeon dexterity. In conjunction with artificial intelligence, these soft robots hold the potential to expedite autonomous interventions, such as tissue palpation for cancer detection. While cameras are prevalent in surgical instruments, situations with obscured views necessitate palpation. This proof-of-concept study investigates the effectiveness of using a soft robot integrated with Electrical Impedance Tomography (EIT) capabilities for tissue palpation in simulated in vivo inspection of the large intestine. The approach involves classifying tissue samples of varying thickness into healthy and cancerous tissues using the shape changes induced on a hydraulically-driven soft continuum robot during palpation. Shape changes of the robot are mapped using EIT, providing arrays of impedance measurements. Following the fabrication of an in-plane bending soft manipulator, the preliminary tissue phantom design is detailed. The phantom, representing the descending colon wall, considers induced stiffness by surrounding tissues based on a mass-spring model. The shape changes of the manipulator, resulting from interactions with tissues of different stiffness, are measured, and EIT measurements are fed into a Long Short-Term Memory (LSTM) classifier. Train and test datasets are collected as temporal sequences of data from a single training phantom and two test phantoms, namely, A and B, possessing distinctive thickness patterns. The collected dataset from phantom B, which differs in stiffness distribution, remains unseen to the network, thus posing challenges to the classifier. The classifier and proposed method achieve an accuracy of 93 % and 88.1 % on phantom A and B, respectively. Classification results are presented through confusion matrices and heat maps, visualising the accuracy of the algorithm and corresponding classified tissues.

Fluidic Multichambered Actuator and Multiaxis Intrinsic Force Sensing.

Soft robots have morphological characteristics that make them preferred candidates, over their traditionally rigid counterparts, for executing physical interaction tasks with the environment. Therefore, equipping them with force sensing is essential for ensuring safety, enhancing their controllability, and adding autonomy. At the same time, it is necessary to preserve their inherent flexibility when integrating sensory units. Soft-fluidic actuators (SFAs) with hydraulic actuation address some of the challenges posed by the compressibility of pneumatic actuation while maintaining system compliance. This research further investigates the feasibility of utilizing the incompressible actuation fluid as the means of actuation and of multiaxial sensing. We have developed a hyperelastic model for the actuation pressure, acting as a baseline pressure. Any disparities from the baseline have been mapped to external forces, using the principle of pressure-based fluidic soft sensor. Computed tomography imaging has been used to examine inner deformation and validate the analytically derived actuation-pressure model. The induced stresses within the SFA are examined using COMSOL simulations, contributing to the development of a calibration algorithm, which accounts for geometric and cross-sectional nonlinearities and maps pressure variations with tip forces. Two force types (concentrated and distributed) acting on our SFA under different configurations are examined, using two experimental setups described as "Point Load" and "Distributed Force." The force sensing algorithm achieves high accuracy with a maximum absolute error of 0.32N for forces with a magnitude of up to 6N.

IRE made easy: introducing the robotic grid system for multiple parallel needle insertion in irreversible electroporation treatment.

PURPOSE: Accurate needle placement is crucial for successful tumor treatment using the irreversible electroporation (IRE) method. Multiple needles are inserted around the tumor, ideally in parallel, to achieve uniform electric field distribution. This paper presents a robot utilizing a grid system to enable multiple needles insertion while maintaining parallelism between them. METHODS: The robotic system has two degrees of freedom, which allow for the adjustment of the grid system to accommodate targeting lesions in various positions. The robot's performance was evaluated by testing its accuracy across various configurations and target depth locations, as well as its ability to maintain the needle parallelism. RESULTS: The robot has dimensions of ϕ 134 mm and a height of 46 mm, with a total weight of 295 g. The system accuracy test showed that the robot can precisely target points across different target depths and needle orientations, with an average error of 2.71 ± 0.68 mm. Moreover, multiple insertions at different grid locations reveal needle orientation deviations typically below 1 ∘ . CONCLUSION: This study presented the design and validation of a robotic grid system. The robot is capable of maintaining insertion accuracy and needle parallelism during multiple needle insertions at various robot configurations. The robot showed promising results with limited needle deviation, making it suitable for IRE procedures.

Handheld robotic device for endoscopic neurosurgery: system integration and pre-clinical evaluation.

The Expanded Endoscopic Endonasal Approach, one of the best examples of endoscopic neurosurgery, allows access to the skull base through the natural orifice of the nostril. Current standard instruments lack articulation limiting operative access and surgeon dexterity, and thus, could benefit from robotic articulation. In this study, a handheld robotic system with a series of detachable end-effectors for this approach is presented. This system is comprised of interchangeable articulated 2/3 degrees-of-freedom 3 mm instruments that expand the operative workspace and enhance the surgeon's dexterity, an ergonomically designed handheld controller with a rotating joystick-body that can be placed at the position most comfortable for the user, and the accompanying control box. The robotic instruments were experimentally evaluated for their workspace, structural integrity, and force-delivery capabilities. The entire system was then tested in a pre-clinical context during a phantom feasibility test, followed up by a cadaveric pilot study by a cohort of surgeons of varied clinical experience. Results from this series of experiments suggested enhanced dexterity and adequate robustness that could be associated with feasibility in a clinical context, as well as improvement over current neurosurgical instruments.

Autonomous control of an ultrasound probe for intra-operative ultrasonography using vision-based shape sensing of pneumatically attachable flexible rails.

PURPOSE: In robotic-assisted minimally invasive surgery, surgeons often use intra-operative ultrasound to visualise endophytic structures and localise resection margins. This must be performed by a highly skilled surgeon. Automating this subtask may reduce the cognitive load for the surgeon and improve patient outcomes. METHODS: We demonstrate vision-based shape sensing of the pneumatically attachable flexible (PAF) rail by using colour-dependent image segmentation. The shape-sensing framework is evaluated on known curves ranging from r = 30 to r = 110 mm, replicating curvatures in a human kidney. The shape sensing is then used to inform path planning of a collaborative robot arm paired with an intra-operative ultrasound probe. We execute 15 autonomous ultrasound scans of a tumour-embedded kidney phantom and retrieve viable ultrasound images, as well as seven freehand ultrasound scans for comparison. RESULTS: The vision-based sensor is shown to have comparable sensing accuracy with FBGS-based systems. We find the RMSE of the vision-based shape sensing of the PAF rail compared with ground truth to be 0.4975 ± 0.4169 mm. The ultrasound images acquired by the robot and by the human were evaluated by two independent clinicians. The median score across all criteria for both readers was '3-good' for human and '4-very good' for robot. CONCLUSION: We have proposed a framework for autonomous intra-operative US scanning using vision-based shape sensing to inform path planning. Ultrasound images were evaluated by clinicians for sharpness of image, clarity of structures visible, and contrast of solid and fluid areas. Clinicians evaluated that robot-acquired images were superior to human-acquired images in all metrics. Future work will translate the framework to a da Vinci surgical robot.

Mixed reality based teleoperation and visualization of surgical robotics.

Surgical robotics has revolutionized the field of surgery, facilitating complex procedures in operating rooms. However, the current teleoperation systems often rely on bulky consoles, which limit the mobility of surgeons. This restriction reduces surgeons' awareness of the patient during procedures and narrows the range of implementation scenarios. To address these challenges, an alternative solution is proposed: a mixed reality-based teleoperation system. This system leverages hand gestures, head motion tracking, and speech commands to enable the teleoperation of surgical robots. The implementation focuses on the da Vinci research kit (dVRK) and utilizes the capabilities of Microsoft HoloLens 2. The system's effectiveness is evaluated through camera navigation tasks and peg transfer tasks. The results indicate that, in comparison to manipulator-based teleoperation, the system demonstrates comparable viability in endoscope teleoperation. However, it falls short in instrument teleoperation, highlighting the need for further improvements in hand gesture recognition and video display quality.

First-in-human real-time AI-assisted instrument deocclusion during augmented reality robotic surgery.

The integration of Augmented Reality (AR) into daily surgical practice is withheld by the correct registration of pre-operative data. This includes intelligent 3D model superposition whilst simultaneously handling real and virtual occlusions caused by the AR overlay. Occlusions can negatively impact surgical safety and as such deteriorate rather than improve surgical care. Robotic surgery is particularly suited to tackle these integration challenges in a stepwise approach as the robotic console allows for different inputs to be displayed in parallel to the surgeon. Nevertheless, real-time de-occlusion requires extensive computational resources which further complicates clinical integration. This work tackles the problem of instrument occlusion and presents, to the authors' best knowledge, the first-in-human on edge deployment of a real-time binary segmentation pipeline during three robot-assisted surgeries: partial nephrectomy, migrated endovascular stent removal, and liver metastasectomy. To this end, a state-of-the-art real-time segmentation and 3D model pipeline was implemented and presented to the surgeon during live surgery. The pipeline allows real-time binary segmentation of 37 non-organic surgical items, which are never occluded during AR. The application features real-time manual 3D model manipulation for correct soft tissue alignment. The proposed pipeline can contribute towards surgical safety, ergonomics, and acceptance of AR in minimally invasive surgery.

Autonomous Magnetic Navigation in Endoscopic Image Mosaics.

Endoscopes navigate within the human body to observe anatomical structures with minimal invasiveness. A major shortcoming of their use is their narrow field-of-view during navigation in large, hollow anatomical regions. Mosaics of endoscopic images can provide surgeons with a map of the tool's environment. This would facilitate procedures, improve their efficiency, and potentially generate better patient outcomes. The emergence of magnetically steered endoscopes opens the way to safer procedures and creates an opportunity to provide robotic assistance both in the generation of the mosaic map and in navigation within this map. This paper proposes methods to autonomously navigate magnetic endoscopes to 1) generate endoscopic image mosaics and 2) use these mosaics as user interfaces to navigate throughout the explored area. These are the first strategies, which allow autonomous magnetic navigation in large, hollow organs during minimally invasive surgeries. The feasibility of these methods is demonstrated experimentally both in vitro and ex vivo in the context of the treatment of twin-to-twin transfusion syndrome. This minimally invasive procedure is performed in utero and necessitates coagulating shared vessels of twin fetuses on the placenta. A mosaic of the vasculature in combination with autonomous navigation has the potential to significantly facilitate this challenging surgery.

Autonomous Needle Navigation in Subretinal Injections via iOCT.

Subretinal injection is an effective method for direct delivery of therapeutic agents to treat prevalent subretinal diseases. Among the challenges for surgeons are physiological hand tremor, difficulty resolving single-micron scale depth perception, and lack of tactile feedback. The recent introduction of intraoperative Optical Coherence Tomography (iOCT) enables precise depth information during subretinal surgery. However, even when relying on iOCT, achieving the required micron-scale precision remains a significant surgical challenge. This work presents a robot-assisted workflow for high-precision autonomous needle navigation for subretinal injection. The workflow includes online registration between robot and iOCT coordinates; tool-tip localization in iOCT coordinates using a Convolutional Neural Network (CNN); and tool-tip planning and tracking system using real-time Model Predictive Control (MPC). The proposed workflow is validated using a silicone eye phantom and ex vivo porcine eyes. The experimental results demonstrate that the mean error to reach the user-defined target and the mean procedure duration are within an acceptable precision range. The proposed workflow achieves a 100% success rate for subretinal injection, while maintaining scleral forces at the scleral insertion point below 15mN throughout the navigation procedures.

Robots for surgeons? Surgeons for robots? Exploring the acceptance of robotic surgery in the light of attitudes and trust in robots.

BACKGROUND: Over the last century, technological progress has been tremendous, and technological advancement is reflected in the development of medicine. This research assessed attitudes towards surgical robots and identified correlations with willingness to participate in robotic surgery based on factors influencing trust in automated systems. METHOD: Using data from a survey, which included the Multi-dimensional Robot Attitude Scale (MdRAS) and a questionnaire consisting of attitude statements regarding the factors affecting trust in automated systems, the experiment assessed the attitudes of healthcare workers and potential patients towards surgery robots, and attempted to find a correlation between these attitudes, age, and gender. RESULTS AND CONCLUSION: Statistical evaluation of the responses (N = 197) showed that positive attitude towards surgical robots showed a high correlation with the willingness to participate in robotic surgery and gave the strongest correlations with the MdRAS utility and negative attitude towards robots subscales. For the assessment of willingness, the MdRAS subscales alone did not provide a strong enough correlation. All factors examined showed a significant correlation with participation. Having faith in the surgery robot, the propensity to trust technology, the designer's reputation, the ease of work that a surgical robot provides, positive experience with robots, and believing the surgeon is competent at operating the machine seemed to have been the most important positive correlations, while fear of errors gave the highest negative correlation. The healthcare workers and potential patients showed significant differences in the subscales of the questionnaire perceived risk and knowledge but no significant difference in the characteristics of the surgical robot. There was no difference in willingness to participate between the samples. Age did not show a significant correlation with the score achieved and willingness in any of the samples. Significant differences were found between male and female respondents, with men having more positive attitudes and being more likely to participate in surgeries using surgery robots than women. As a result, the research potentially sheds light on the factors that need to be considered when building trust in robotic surgery.

Fast-Response Variable-Stiffness Magnetic Catheters for Minimally Invasive Surgery.

In minimally invasive surgery, such as cardiac ablation, magnetically steered catheters made of variable-stiffness materials can enable higher dexterity and higher force application to human tissue. However, the long transition time between soft and rigid states leads to a significant increase in procedure duration. Here, a fast-response, multisegmented catheter is described for minimally invasive surgery made of variable-stiffness thread (FRVST) that encapsulates a helical cooling channel. The rapid stiffness change in the FRVST, composed of a nontoxic shape memory polymer, is achieved by an active cooling system that pumps water through the helical channel. The FRVST displays a 66 times stiffness change and a 26 times transition enhancement compare with the noncooled version. The catheter allows for selective bending of each segment up to 127° in air and up to 76° in water under an 80 mT external magnetic field. The inner working channel can be used for cooling an ablation tip during a procedure and for information exchange via the deployment of wires or surgical tools.

Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing.

In the last decade, various robotic platforms have been introduced that could support delicate retinal surgeries. Concurrently, to provide semantic understanding of the surgical area, recent advances have enabled microscope-integrated intraoperative Optical Coherent Tomography (iOCT) with high-resolution 3D imaging at near video rate. The combination of robotics and semantic understanding enables task autonomy in robotic retinal surgery, such as for subretinal injection. This procedure requires precise needle insertion for best treatment outcomes. However, merging robotic systems with iOCT introduces new challenges. These include, but are not limited to high demands on data processing rates and dynamic registration of these systems during the procedure. In this work, we propose a framework for autonomous robotic navigation for subretinal injection, based on intelligent real-time processing of iOCT volumes. Our method consists of an instrument pose estimation method, an online registration between the robotic and the iOCT system, and trajectory planning tailored for navigation to an injection target. We also introduce intelligent virtual B-scans, a volume slicing approach for rapid instrument pose estimation, which is enabled by Convolutional Neural Networks (CNNs). Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method. Finally, we discuss identified challenges in this work and suggest potential solutions to further the development of such systems.

Implantable magnetically-actuated capsule for on-demand delivery.

Many implantable drug delivery systems (IDDS) have been developed for long-term, pulsatile drug release. However, they are often limited by bulky size, complex electronic components, unpredictable drug delivery, as well as the need for battery replacement and consequent replacement surgery. Here, we develop an implantable magnetically-actuated capsule (IMAC) and its portable magnetic actuator (MA) for on-demand and robust drug delivery in a tether-free and battery-free manner. IMAC utilizes the bistable mechanism of two magnetic balls inside IMAC to trigger drug delivery under a strong magnetic field (|Ba| > 90 mT), ensuring precise and reproducible drug delivery (9.9 ± 0.17 µg per actuation, maximum actuation number: 180) and excellent anti-magnetic capability (critical trigger field intensity: ∼90 mT). IMAC as a tetherless robot can navigate to and anchor at the lesion sites driven by a gradient magnetic field (∇ Bg = 3 T/m, |Bg| < 60 mT), and on-demand release drug actuated by a uniform magnetic field (|Ba| = âˆ¼100 mT) within the gastrointestinal tract. During a 15-day insulin administration in vivo, the diabetic rats treated with IMAC exhibited highly similar pharmacokinetic and pharmacodynamic profiles to those administrated via subcutaneous injection, demonstrating its robust and on-demand drug release performance. Moreover, IMAC is biocompatible, batter-free, refillable, miniature (only Φ 6.3 × 12.3 mm3), and lightweight (just 0.8 g), making it an ideal alternative for precise implantable drug delivery and friendly patient-centered drug administration.

Preliminary evaluation for ultrasound-guided targeted prostate biopsy using a portable surgical robot: Ex vivo results.

BACKGROUND: Robotic systems are increasingly used to enhance clinical outcomes in prostate intervention. To evaluate the clinical value of the proposed portable robot, the robot-assisted and robot-targeted punctures were validated experimentally. METHOD: The robot registration utilising the electromagnetic tracker achieves coordinate transformation from the ultrasound (US) image to the robot. Subsequently, Transrectal ultrasound (TRUS)-guided phantom trials were conducted for robot-assisted, free-hand, and robot-targeted punctures. RESULTS: The accuracy of robot registration was 0.95 mm, and the accuracy of robot-assisted, free-hand, and robot-targeted punctures was 2.38 ± 0.64 mm, 3.11 ± 0.72 mm, and 3.29 ± 0.83 mm sequentially. CONCLUSION: The registration method has been successfully applied to robot-targeted puncture. Current results indicate that the accuracy of robot-targeted puncture is slightly inferior to that of manual operations. Moreover, in manual operation, robot-assisted puncture improves the accuracy of free-hand puncture. Accuracy superior to 3.5 mm demonstrates the clinical applicability of both robot-assisted and robot-targeted punctures.