Opportunities and Barriers to Rural Telerobotic Surgical Health Care in 2021: Report and Research Agenda from a Stakeholder Workshop.

Background: There are well-recognized challenges to delivering specialty health care in rural settings. These challenges are particularly evident for specialized surgical health care due to the lack of trained operators in rural communities. Telerobotic surgery could have a significant impact on the rural-urban health care gap, but thus far, the promise of this method of health care delivery has gone unrealized. With the increasing adoption of telehealth over the past year, along with the maturation of telecommunication and robotic technologies over the past 2 decades, a reappraisal of the opportunities and barriers to widespread implementation of telerobotic surgery is warranted. Here we report the outcome of a rural telerobotic stakeholder workshop to explore modern-day issues critical to the advancement of telerobotic surgical health care. Materials and Methods: We assembled a multidisciplinary stakeholder panel to participate in a 2-day Rural Telerobotic Surgery Stakeholder Workshop. Participants had diverse expertise, including specialty surgeons, technology experts, and representatives of the broader telerobotic health care ecosystem, including economists, lawyers, regulatory consultants, public health advocates, rural hospital administrators, nurses, and payers. The research team reviewed transcripts from the workshop with themes identified and research questions generated based on stakeholder comments and feedback. Results: Stakeholder discussions fell into four general themes, including (1) operating room team interactions, (2) education and training, (3) network and security, and (4) economic issues. The research team then identified several research questions within each of these themes and provided specific research strategies to address these questions. Conclusions: There are still important unanswered questions regarding the implementation and adoption of rural telerobotic surgery. Based on stakeholder feedback, we have developed a research agenda along with suggested strategies to address outstanding research questions. The successful execution of these research opportunities will fill critical gaps in our understanding of how to advance the widespread adoption of rural telerobotic health care.

Local Style Preservation in Improved GAN-Driven Synthetic Image Generation for Endoscopic Tool Segmentation.

Accurate semantic image segmentation from medical imaging can enable intelligent vision-based assistance in robot-assisted minimally invasive surgery. The human body and surgical procedures are highly dynamic. While machine-vision presents a promising approach, sufficiently large training image sets for robust performance are either costly or unavailable. This work examines three novel generative adversarial network (GAN) methods of providing usable synthetic tool images using only surgical background images and a few real tool images. The best of these three novel approaches generates realistic tool textures while preserving local background content by incorporating both a style preservation and a content loss component into the proposed multi-level loss function. The approach is quantitatively evaluated, and results suggest that the synthetically generated training tool images enhance UNet tool segmentation performance. More specifically, with a random set of 100 cadaver and live endoscopic images from the University of Washington Sinus Dataset, the UNet trained with synthetically generated images using the presented method resulted in 35.7% and 30.6% improvement over using purely real images in mean Dice coefficient and Intersection over Union scores, respectively. This study is promising towards the use of more widely available and routine screening endoscopy to preoperatively generate synthetic training tool images for intraoperative UNet tool segmentation.

A Model-Based Recurrent Neural Network With Randomness for Efficient Control With Applications.

Recently, Recurrent Neural Network (RNN) control schemes for redundant manipulators have been extensively studied. These control schemes demonstrate superior computational efficiency, control precision, and control robustness. However, they lack planning completeness. This paper explains why RNN control schemes suffer from the problem. Based on the analysis, this work presents a new random RNN control scheme, which 1) introduces randomness into RNN to address the planning completeness problem, 2) improves control precision with a new optimization target, 3) improves planning efficiency through learning from exploration. Theoretical analyses are used to prove the global stability, the planning completeness, and the computational complexity of the proposed method. Software simulation is provided to demonstrate the improved robustness against noise, the planning completeness and the improved planning efficiency of the proposed method over benchmark RNN control schemes. Real-world experiments are presented to demonstrate the application of the proposed method.

Automated Surgical Approach Planning for Complex Skull Base Targets: Development and Validation of a Cost Function and Semantic At-las.

Successful multidisciplinary treatment of skull base pathology requires precise preoperative planning. Current surgical approach (pathway) selection for these complex procedures depends on an individual surgeon's experiences and background training. Because of anatomical variation in both normal tissue and pathology (eg, tumor), a successful surgical pathway used on one patient is not necessarily the best approach on another patient. The question is how to define and obtain optimized patient-specific surgical approach pathways? In this article, we demonstrate that the surgeon's knowledge and decision making in preoperative planning can be modeled by a multiobjective cost function in a retrospective analysis of actual complex skull base cases. Two different approaches- weighted-sum approach and Pareto optimality-were used with a defined cost function to derive optimized surgical pathways based on preoperative computed tomography (CT) scans and manually designated pathology. With the first method, surgeon's preferences were input as a set of weights for each objective before the search. In the second approach, the surgeon's preferences were used to select a surgical pathway from the computed Pareto optimal set. Using preoperative CT and magnetic resonance imaging, the patient-specific surgical pathways derived by these methods were similar (85% agreement) to the actual approaches performed on patients. In one case where the actual surgical approach was different, revision surgery was required and was performed utilizing the computationally derived approach pathway.

Quantitative Analysis of Transnasal Anterior Skull Base Approach: Report of Technology for Intraoperative Assessment of Instrument Motion.

OBJECTIVE: To develop a method to measure intraoperative surgical instrument motion. This model will be applicable to the study of surgical instrument kinematics including surgical training, skill verification, and the development of surgical warning systems that detect aberrant instrument motion that may result in patient injury. DESIGN: We developed an algorithm to automate derivation of surgical instrument kinematics in an endoscopic endonasal skull base surgery model. Surgical instrument motion was recorded during a cadaveric endoscopic transnasal approach to the pituitary using a navigation system modified to record intraoperative time-stamped Euclidian coordinates and Euler angles. Microdebrider tip coordinates and angles were referenced to the cadaver's preoperative computed tomography scan allowing us to assess surgical instrument kinematics over time. A representative cadaveric endoscopic endonasal approach to the pituitary was performed to demonstrate feasibility of our algorithm for deriving surgical instrument kinematics. CONCLUSIONS: Technical feasibility of automatically measuring intraoperative surgical instrument motion and deriving kinematics measurements was demonstrated using standard navigation equipment.

Crowd-sourced assessment of surgical skills in cricothyrotomy procedure.

BACKGROUND: Objective assessment of surgical skills is resource intensive and requires valuable time of expert surgeons. The goal of this study was to assess the ability of a large group of laypersons using a crowd-sourcing tool to grade a surgical procedure (cricothyrotomy) performed on a simulator. The grading included an assessment of the entire procedure by completing an objective assessment of technical skills survey. MATERIALS AND METHODS: Two groups of graders were recruited as follows: (1) Amazon Mechanical Turk users and (2) three expert surgeons from University of Washington Department of Otolaryngology. Graders were presented with a video of participants performing the procedure on the simulator and were asked to grade the video using the objective assessment of technical skills questions. Mechanical Turk users were paid $0.50 for each completed survey. It took 10 h to obtain all responses from 30 Mechanical Turk users for 26 training participants (26 videos/tasks), whereas it took 60 d for three expert surgeons to complete the same 26 tasks. RESULTS: The assessment of surgical performance by a group (n = 30) of laypersons matched the assessment by a group (n = 3) of expert surgeons with a good level of agreement determined by Cronbach alpha coefficient = 0.83. CONCLUSIONS: We found crowd sourcing was an efficient, accurate, and inexpensive method for skills assessment with a good level of agreement to experts' grading.

Beyond task time: automated measurement augments fundamentals of laparoscopic skills methodology.

BACKGROUND: Laparoscopic psychomotor skills are challenging to learn and objectively evaluate. The Fundamentals of Laparoscopic Skills (FLS) program provides a popular, inexpensive, widely-studied, and reported method for evaluating basic laparoscopic skills. With an emphasis on training safety before efficiency, we present data that explore the metrics in the FLS curriculum. MATERIALS AND METHODS: A multi-institutional (n = 3) cross-sectional study enrolled subjects (n = 98) of all laparoscopic skill levels to perform FLS tasks in an instrumented box trainer. Recorded task videos were postevaluated by faculty reviewers (n = 2) blinded to subject identity using a modified Objective Structured Assessment of Technical Skills (OSATS) protocol. FLS scores were computed for each completed task and compared with demographically established skill levels (training level and number of procedures), video review scoring, and objective performance metrics including path length, economy of motion, and peak grasping force. RESULTS: Three criteria used to determine expert skill, training and experience level, blinded review of performance by faculty via OSATS, and FLS scores, disagree in establishing concurrent validity for determining "true experts" in FLS tasks. FLS-scoring exhibited near-perfect correlation with task time for all three tasks (Pearson r = 0.99, 1.00, 1.00 with P <0.00000001). FLS error penalties had negligible effect on FLS scores. Peak grasping force did not correlate with task time or FLS scores. CONCLUSIONS: FLS technical skills scores presented negligible benefit beyond the measurement of task time. FLS scoring is weighted more toward speed than precision and may not significantly address poor tissue handling skills, especially regarding excessive grasping force. Categories of experience or training level may not form a suitable basis for establishing proficiency thresholds or for construct validity studies for technical skills.

Multiportal Retrograde Endoscopy to Enhance Surgical Target Visualization: A Pilot Study.

Objective Current transnasal endoscopic techniques for sinus and skull base surgery use a single endoscope to provide visualization from one perspective curtailing depth perception and compromising visualization of the instrument-target interface. The view can be blocked by instruments, and collisions between instruments often occur. The objective of this study was to investigate the use of multiportal retrograde endoscopy to provide more accurate manipulation of the surgical target. Design Maxillary antrostomy and frontal sinusotomy were performed on three different cadavers by three different surgeons. A zero-degree rigid endoscope was introduced through the nose for the standard transnasal approach. A flexible endoscope was introduced transorally, directed past the palate superiorly, and then flexed 180 degrees for the retrograde view. Videos of the standard transnasal view from the rigid endoscope and retrograde view from the flexible endoscope were recorded simultaneously. Results All surgeries were able to be performed with dual-screen viewing of the standard and retrograde view. The surgeons noted that they utilized the retrograde view to adjust the location of ends/tips of their instruments. Four surgeons reviewed the videos and individually agreed that the visualization achieved provided a perspective otherwise not attainable with rigid transnasal endoscopy alone. Conclusion High-quality visualization of surgical targets such as the frontal or maxillary ostia can be challenging with rigid endoscopes alone. Multiportal retrograde endoscopy provides proof of concept that additional views of a surgical target can be achieved. Additional work is needed to further develop indications, techniques, and generalizability to targets beyond those investigated here.

Reducing annotating load: Active learning with synthetic images in surgical instrument segmentation.

Accurate instrument segmentation in the endoscopic vision of minimally invasive surgery is challenging due to complex instruments and environments. Deep learning techniques have shown competitive performance in recent years. However, deep learning usually requires a large amount of labeled data to achieve accurate prediction, which poses a significant workload. To alleviate this workload, we propose an active learning-based framework to generate synthetic images for efficient neural network training. In each active learning iteration, a small number of informative unlabeled images are first queried by active learning and manually labeled. Next, synthetic images are generated based on these selected images. The instruments and backgrounds are cropped out and randomly combined with blending and fusion near the boundary. The proposed method leverages the advantage of both active learning and synthetic images. The effectiveness of the proposed method is validated on two sinus surgery datasets and one intraabdominal surgery dataset. The results indicate a considerable performance improvement, especially when the size of the annotated dataset is small. All the code is open-sourced at: https://github.com/HaonanPeng/active_syn_generator.

Instrument Failures for the da Vinci Surgical System: a Food and Drug Administration MAUDE Database Study.

BACKGROUND: Our goal was to analyze reported instances of the da Vinci robotic surgical system instrument failures using the FDA's MAUDE (Manufacturer and User Facility Device Experience) database. From these data we identified some root causes of failures as well as trends that may assist surgeons and users of the robotic technology. METHODS: We conducted a survey of the MAUDE database and tallied robotic instrument failures that occurred between January 2009 and December 2010. We categorized failures into five main groups (cautery, shaft, wrist or tool tip, cable, and control housing) based on technical differences in instrument design and function. RESULTS: A total of 565 instrument failures were documented through 528 reports. The majority of failures (285) were of the instrument's wrist or tool tip. Cautery problems comprised 174 failures, 76 were shaft failures, 29 were cable failures, and 7 were control housing failures. Of the reports, 10 had no discernible failure mode and 49 exhibited multiple failures. CONCLUSIONS: The data show that a number of robotic instrument failures occurred in a short period of time. In reality, many instrument failures may go unreported, thus a true failure rate cannot be determined from these data. However, education of hospital administrators, operating room staff, surgeons, and patients should be incorporated into discussions regarding the introduction and utilization of robotic technology. We recommend institutions incorporate standard failure reporting policies so that the community of robotic surgery companies and surgeons can improve on existing technologies for optimal patient safety and outcomes.

Object-Agnostic Vision Measurement Framework Based on One-Shot Learning and Behavior Tree.

Vision measurement is important for intelligent systems to obtain the precise structural and spatial information of objects. Beyond the object-specific vision measurement developed for fixed object type, it is appealing to explore the object-agnostic vision measurement, which can be efficiently reconfigured and adapted to various novel objects. This article proposes a framework to mimic the human's versatile visual measurement behavior: extract a set of contour primitives of interest (CPIs) from an image, then utilize the CPIs to calculate the key geometric information. First, a deep convolutional neural network (CNN) CPieNet+ is proposed under the one-shot learning scheme, aiming to extract the pixel-level object CPI from a raw query image, given an annotated support image. The fine-grained CPI prototypes are formed by sampling multiple points on the feature map of the support image. To leverage the explicit geometric knowledge in the CNN inference, the annotation map is encoded as a shape descriptor to guide the feature channel attention, and the geometric attribute awareness is realized by supervising the model to predict the direction and size of CPI. Second, the measurement behavior tree (BT) is designed to model the hierarchical geometric calculation procedure, which is flexibly configurable for different measurement requirements and is interpretable for nonexpert users. After the execution of the measurement BT, the pixel-level CPIs are converted to the required key geometric data. The effectiveness of the proposed methods is validated by a series of experiments.

Automatic summarization of endoscopic skull base surgical videos through object detection and hidden Markov modeling.

Endoscopic endonasal surgery is a medical procedure that utilizes an endoscopic video camera to view and manipulate a surgical site accessed through the nose. Despite these surgeries being video recorded, these videos are seldom reviewed or even saved in patient files due to the size and length of the video file. Editing to a manageable size may necessitate viewing 3 h or more of surgical video and manually splicing together the desired segments. We suggest a novel multi-stage video summarization procedure utilizing deep semantic features, tool detections, and video frame temporal correspondences to create a representative summarization. Summarization by our method resulted in a 98.2% reduction in overall video length while preserving 84% of key medical scenes. Furthermore, resulting summaries contained only 1% of scenes with irrelevant detail such as endoscope lens cleaning, blurry frames, or frames external to the patient. This outperformed leading commercial and open source summarization tools not designed for surgery, which only preserved 57% and 46% of key medical scenes in similar length summaries, and included 36% and 59% of scenes containing irrelevant detail. Experts agreed that on average (Likert Scale = 4) that the overall quality of the video was adequate to share with peers in its current state.

Real-time virtual intraoperative CT in endoscopic sinus surgery.

PURPOSE: Endoscopic sinus surgery (ESS) is typically guided under preoperative computed tomography (CT), which increasingly diverges from actual patient anatomy as the surgery progresses. Studies have reported that the revision surgery rate in ESS ranges between 28 and 47%. This paper presents a method that can update the preoperative CT in real time to improve surgical completeness in ESS. APPROACH: The work presents and compares three novel methods that use instrument motion data and anatomical structures to predict surgical modifications in real time. The methods use learning techniques, such as nonparametric filtering and Gaussian process regression, to correlate surgical modifications with instrument tip positions, tip trajectories, and instrument shapes. Preoperative CT image sets are updated with modification predictions to serve as a virtual intraoperative CT. RESULTS: The three methods were compared in eight ESS cadaver cases, which were performed by five surgeons and included the following representative ESS operations: maxillary antrostomy, uncinectomy, anterior and posterior ethmoidectomy, and sphenoidotomy. Experimental results showed accuracy metrics that were clinically acceptable with dice similarity coefficients > 86%, with F-score > 92% and precision > 89.91% in surgical completeness evaluation. Among the three methods, the tip trajectory-based estimator had the highest precision of 96.87%. CONCLUSIONS: This work demonstrated that virtually modified intraoperative CT scans improved the consistency between the actual surgical scene and the reference model, and could lead to improved surgical completeness in ESS. Compared to actual intraoperative CT scans, the proposed method has no impact on existing surgical protocols, does not require extra hardware, does not expose the patient to radiation, and does not lengthen time under anesthesia.

Exploring telerobotic cardiac catheter ablation in a rural community hospital: A pilot study.

Background: Telerobotic surgery could improve access to specialty procedures such as cardiac catheter ablation in rural and underserved regions in the United States and worldwide. Advancements in telecommunications, internet infrastructure, and surgical robotics are lowering the technical hurdles for this future healthcare delivery paradigm. Nonetheless, important questions remain regarding the safe implementation of telerobotic surgery in rural community hospital settings. Objective: The purpose of this study was to pilot test a system and methods to explore telerobotic cardiac catheter ablation in a rural community hospital setting. Methods: We assembled a portable preclinical telerobotic catheter ablation system from commercial-grade components using third-party vendors. We then carried out 4 telerobotic surgery simulations with an urban surgeon and a rural community hospital operating room (OR) team spanning a distance of more than 2000 miles. Two challenge scenarios were incorporated into the simulations, including loss of network connection and cardiac perforation with subsequent life-threatening tamponade physiology. An ethnographic analysis was then performed. Results: Interviews and observations suggested that rural OR teams readily adapt to the telesurgery context. However, participant perceptions of team trust, communication, and emergency management were significantly altered by the remote location of the surgeon. In addition, most participants believed the OR team would have been better equipped for the challenges had they received formal training or had prior experience with the procedure being simulated. Conclusion: We demonstrate the utility and feasibility of a system and methods for studying specialty telerobotic surgery in a rural hospital OR setting.

Automated atlas-based segmentation for skull base surgical planning.

PURPOSE: Computational surgical planning tools could help develop novel skull base surgical approaches that improve safety and patient outcomes. This defines a need for automated skull base segmentation to improve the usability of surgical planning software. The objective of this work was to design and validate an algorithm for atlas-based automated segmentation of skull base structures in individual image sets for skull base surgical planning. METHODS: Advanced Normalization Tools software was used to construct a synthetic CT template from 6 subjects, and skull base structures were manually segmented to create a reference atlas. Landmark registration followed by Elastix deformable registration was applied to the template to register it to each of the 30 trusted reference image sets. Dice coefficient, average Hausdorff distance, and clinical usability scoring were used to compare the atlas segmentations to those of the trusted reference image sets. RESULTS: The mean for average Hausdorff distance for all structures was less than 2 mm (mean for 95th percentile Hausdorff distance was less than 5 mm). For structures greater than 2.5 mL in volume, the average Dice coefficient was 0.73 (range 0.59-0.82), and for structures less than 2.5 mL in volume the Dice coefficient was less than 0.7. The usability scoring survey was completed by three experts, and all structures met the criteria for acceptable effort except for the foramen spinosum, rotundum, and carotid artery, which required more than minor corrections. CONCLUSION: Currently available open-source algorithms, such as the Elastix deformable algorithm, can be used for automated atlas-based segmentation of skull base structures with acceptable clinical accuracy and minimal corrections with the use of the proposed atlas. The first publicly available CT template and anterior skull base segmentation atlas being released (available at this link: http://hdl.handle.net/1773/46259 ) with this paper will allow for general use of automated atlas-based segmentation of the skull base.

A decade retrospective of medical robotics research from 2010 to 2020.

Robotics is a forward-looking discipline. Attention is focused on identifying the next grand challenges. In an applied field such as medical robotics, however, it is important to plan the future based on a clear understanding of what the research community has recently accomplished and where this work stands with respect to clinical needs and commercialization. This Review article identifies and analyzes the eight key research themes in medical robotics over the past decade. These thematic areas were identified using search criteria that identified the most highly cited papers of the decade. Our goal for this Review article is to provide an accessible way for readers to quickly appreciate some of the most exciting accomplishments in medical robotics over the past decade; for this reason, we have focused only on a small number of seminal papers in each thematic area. We hope that this article serves to foster an entrepreneurial spirit in researchers to reduce the widening gap between research and translation.

The Future of Skull Base Surgery: A View Through Tinted Glasses.

In the present report, we have broadly outlined the potential advances in the field of skull base surgery, which might occur within the next 20 years based on the many areas of current research in biology and technology. Many of these advances will also be broadly applicable to other areas of neurosurgery. We have grounded our predictions for future developments in an exploration of what patients and surgeons most desire as outcomes for care. We next examined the recent developments in the field and outlined several promising areas of future improvement in skull base surgery, per se, as well as identifying the new hospital support systems needed to accommodate these changes. These include, but are not limited to, advances in imaging, Raman spectroscopy and microscopy, 3-dimensional printing and rapid prototyping, master-slave and semiautonomous robots, artificial intelligence applications in all areas of medicine, telemedicine, and green technologies in hospitals. In addition, we have reviewed the therapeutic approaches using nanotechnology, genetic engineering, antitumor antibodies, and stem cell technologies to repair damage caused by traumatic injuries, tumors, and iatrogenic injuries to the brain and cranial nerves. Additionally, we have discussed the training requirements for future skull base surgeons and stressed the need for adaptability and change. However, the essential requirements for skull base surgeons will remain unchanged, including knowledge, attention to detail, technical skill, innovation, judgment, and compassion. We believe that active involvement in these rapidly evolving technologies will enable us to shape some of the future of our discipline to address the needs of both patients and our profession.

Comparison of Micro-Computed Tomography and Clinical Computed Tomography Protocols for Visualization of Nasal Cartilage Before Surgical Planning for Rhinoplasty.

IMPORTANCE: There is no imaging standard to model nasal cartilage for the planning of rhinoplasty procedures. Preoperative visualization of cartilage may improve objective evaluation of nasal deformities, surgical planning, and surgical reconstruction. OBJECTIVES: To evaluate the feasibility of visualizing nasal cartilage using high resolution micro-computed tomography (CT) compared with the criterion standard of pathologic findings in a cadaveric specimen and to evaluate its accuracy compared with various clinical CT protocols. DESIGN, SETTING, AND PARTICIPANTS: Anatomic study at the University of Washington using single human cadaveric nasal specimens performed from July 10, 2017, to March 30, 2018. INTERVENTIONS: A micro-CT acquisition with 60-micron resolution was obtained of a nasal specimen. The specimen was then scanned with 5 different clinical CT protocols to span both clinical care and machine limits. The specimen was then sectioned in 5-mm axial slices for pathologic analysis. MAIN OUTCOMES AND MEASURES: Micro-CT images were registered to pathologic specimen cross-sections using a graphite fiducial system. Cartilage substructures were manually segmented and analyzed. A library of matched images across the micro-CT and various clinical CT protocols was then developed. Region of interest analysis was performed for each of the cartilage structures and their boundaries on clinical CT protocols and micro-CT, with the outcome of mean (SD) density using Hounsfield units. RESULTS: A single human cadaveric nasal specimen was used to obtain the following results. Lower lateral cartilage, upper lateral cartilage, and septal cartilage were accurately delineated on the micro-CT images compared with pathologic findings. The mean absolute deviation from pathologic findings was 0.30 mm for septal cartilage thickness, 0.98 mm for maximal upper lateral cartilage length, and 1.40 mm for maximal lower lateral cartilage length. On clinical CT protocols, only septal cartilage was well discriminated from boundary. Higher radiation dose resulted in more accurate density measurements of cartilage, but it did not ultimately improve ability to discriminate cartilage. CONCLUSIONS AND RELEVANCE: The results of this anatomic study may represent a notable step toward advancing knowledge of the capabilities and pitfalls of nasal cartilage visualization on CT. Nasal cartilage visualization was feasible on the micro-CT compared with pathologic findings. Future research may further examine the barriers to accurately visualizing upper lateral cartilage and lower lateral cartilage, a prerequisite for clinical application. LEVEL OF EVIDENCE: NA.

Evaluation of unmanned airborne vehicles and mobile robotic telesurgery in an extreme environment.

As unmanned extraction vehicles become a reality in the military theater, opportunities to augment medical operations with telesurgical robotics become more plausible. This project demonstrated an experimental surgical robot using an unmanned airborne vehicle (UAV) as a network topology. Because battlefield operations are dynamic and geographically challenging, the installation of wireless networks is not a feasible option at this point. However, to utilize telesurgical robotics to assist in the urgent medical care of wounded soldiers, a robust, high bandwidth, low latency network is requisite. For the first time, a mobile surgical robotic system was deployed to an austere environment and surgeons were able to remotely operate the systems wirelessly using a UAV. Two University of Cincinnati surgeons were able to remotely drive the University of Washington's RAVEN robot's end effectors. The network topology demonstrated a highly portable, quickly deployable, bandwidth-sufficient and low latency wireless network required for battlefield use.

The biomechanics of percutaneous needle insertion.

Emphasis has been placed on improving patient outcomes in healthcare management. Significant patient morbidity and mortality exists from inappropriate procedural technique and percutaneous catheter needle insertion procedures have been linked to medical complications. Healthcare trainees learn these procedures through trial and error and most existing simulators are synthetic tissue based and lack in-vivo force feedback. We seek to utilize the Blue DRAGON instrument positioning system coupled with a force sensor to determine true forces experienced by a needle as it is passed through animal and human tissues in an effort to design a percutaneous needle insertion simulator that affords the learner with the experience of the true force feedback. Acquiring force displacement measurements of needle insertion is the first step towards development of a computational model of the phenomena. The computational model may be further incorporated into a medical haptic simulator that provides physically based force feedback to the user.