The Experts below are selected from a list of 150 Experts worldwide ranked by ideXlab platform
Robert J. Webster - One of the best experts on this subject based on the ideXlab platform.
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A bimanual teleoperated system for endonasal skull base surgery
IEEE International Conference on Intelligent Robots and Systems, 2011Co-Authors: Jessica Burgner, D. Caleb Rucker, Philip J. Swaney, Hunter B. Gilbert, Scott T. Nill, Paul T Russell, Kyle D Weaver, Robert J. WebsterAbstract:We describe transnasal skull base surgery, including the current clinical procedure and the ways in which a robotic system has the potential to enhance the current standard of care. The available workspace is characterized by segmenting medical images and reconstructing the available 3D geometry. We then describe thin, “tentacle-like” robotic tools with shafts constructed from concentric tube robots, and an actuation unit designed to robotically control them in a teleoperated setting. Lastly, we discuss the results of a proof-of-concept study in a Cadaveric Specimen, illustrating the ability of the robot to access clinically relevant skull base targets.
Davide Bardana - One of the best experts on this subject based on the ideXlab platform.
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Virtual reality compared with bench-top simulation in the acquisition of arthroscopic skill: A randomized controlled trial
Journal of Bone and Joint Surgery - American Volume, 2017Co-Authors: Daniel Banaszek, Daniel You, Justues Chang, Michael Pickell, Daniel Hesse, Wilma M Hopman, Daniel Borschneck, Davide BardanaAbstract:BACKGROUND: Work-hour restrictions as set forth by the Accreditation Council for Graduate Medical Education (ACGME) and other governing bodies have forced training programs to seek out new learning tools to accelerate acquisition of both medical skills and knowledge. As a result, competency-based training has become an important part of residency training. The purpose of this study was to directly compare arthroscopic skill acquisition in both high-fidelity and low-fidelity simulator models and to assess skill transfer from either modality to a Cadaveric Specimen, simulating intraoperative conditions. METHODS: Forty surgical novices (pre-clerkship-level medical students) voluntarily participated in this trial. Baseline demographic data, as well as data on arthroscopic knowledge and skill, were collected prior to training. Subjects were randomized to 5-week independent training sessions on a high-fidelity virtual reality arthroscopic simulator or on a bench-top arthroscopic setup, or to an untrained control group. Post-training, subjects were asked to perform a diagnostic arthroscopy on both simulators and in a simulated intraoperative environment on a Cadaveric knee. A more difficult surprise task was also incorporated to evaluate skill transfer. Subjects were evaluated using the Global Rating Scale (GRS), the 14-point arthroscopic checklist, and a timer to determine procedural efficiency (time per task). Secondary outcomes focused on objective measures of virtual reality simulator motion analysis. RESULTS: Trainees on both simulators demonstrated a significant improvement (p < 0.05) in arthroscopic skills compared with baseline scores and untrained controls, both in and ex vivo. The virtual reality simulation group consistently outperformed the bench-top model group in the diagnostic arthroscopy crossover tests and in the simulated Cadaveric setup. Furthermore, the virtual reality group demonstrated superior skill transfer in the surprise skill transfer task. CONCLUSIONS: Both high-fidelity and low-fidelity simulation trainings were effective in arthroscopic skill acquisition. High-fidelity virtual reality simulation was superior to bench-top simulation in the acquisition of arthroscopic skills, both in the laboratory and in vivo. Further clinical investigation is needed to interpret the importance of these results.
Steven L Giannotta - One of the best experts on this subject based on the ideXlab platform.
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perfusion based human Cadaveric Specimen as a simulation training model in repairing cerebrospinal fluid leaks during endoscopic endonasal skull base surgery
Journal of Neurosurgery, 2017Co-Authors: Eisha Christian, Joshua Bakhsheshian, Ben A Strickland, Vance Fredrickson, Ian A Buchanan, Martin H Pham, Andrew Cervantes, Michael Minneti, Bozena Wrobel, Steven L GiannottaAbstract:OBJECTIVECompetency in endoscopic endonasal approaches (EEAs) to repair high-flow cerebrospinal fluid (CSF) leaks is an essential component of the neurosurgical training process. The objective of this study was to demonstrate the feasibility of a simulation model for EEA repair of anterior skull base CSF leaks.METHODSHuman Cadaveric Specimens were utilized with a perfusion system to simulate a high-flow CSF leak. Neurological surgery residents (postgraduate year 3 or greater) performed a standard EEA to repair a CSF leak using a combination of fat, fascia lata, and pedicled nasoseptal flaps. A standardized 5-point Likert questionnaire was used to assess the knowledge gained, techniques learned, degree of safety, benefit of CSF perfusion during repair, and pre- and posttraining confidence scores.RESULTSIntrathecal perfusion of fluorescein-infused saline into the ventricular/subarachnoid space was successful in 9 of 9 cases. The addition of CSF reconstitution offered the residents visual feedback for confir...
Jessica Burgner - One of the best experts on this subject based on the ideXlab platform.
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A bimanual teleoperated system for endonasal skull base surgery
IEEE International Conference on Intelligent Robots and Systems, 2011Co-Authors: Jessica Burgner, D. Caleb Rucker, Philip J. Swaney, Hunter B. Gilbert, Scott T. Nill, Paul T Russell, Kyle D Weaver, Robert J. WebsterAbstract:We describe transnasal skull base surgery, including the current clinical procedure and the ways in which a robotic system has the potential to enhance the current standard of care. The available workspace is characterized by segmenting medical images and reconstructing the available 3D geometry. We then describe thin, “tentacle-like” robotic tools with shafts constructed from concentric tube robots, and an actuation unit designed to robotically control them in a teleoperated setting. Lastly, we discuss the results of a proof-of-concept study in a Cadaveric Specimen, illustrating the ability of the robot to access clinically relevant skull base targets.
Daniel Banaszek - One of the best experts on this subject based on the ideXlab platform.
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Virtual reality compared with bench-top simulation in the acquisition of arthroscopic skill: A randomized controlled trial
Journal of Bone and Joint Surgery - American Volume, 2017Co-Authors: Daniel Banaszek, Daniel You, Justues Chang, Michael Pickell, Daniel Hesse, Wilma M Hopman, Daniel Borschneck, Davide BardanaAbstract:BACKGROUND: Work-hour restrictions as set forth by the Accreditation Council for Graduate Medical Education (ACGME) and other governing bodies have forced training programs to seek out new learning tools to accelerate acquisition of both medical skills and knowledge. As a result, competency-based training has become an important part of residency training. The purpose of this study was to directly compare arthroscopic skill acquisition in both high-fidelity and low-fidelity simulator models and to assess skill transfer from either modality to a Cadaveric Specimen, simulating intraoperative conditions. METHODS: Forty surgical novices (pre-clerkship-level medical students) voluntarily participated in this trial. Baseline demographic data, as well as data on arthroscopic knowledge and skill, were collected prior to training. Subjects were randomized to 5-week independent training sessions on a high-fidelity virtual reality arthroscopic simulator or on a bench-top arthroscopic setup, or to an untrained control group. Post-training, subjects were asked to perform a diagnostic arthroscopy on both simulators and in a simulated intraoperative environment on a Cadaveric knee. A more difficult surprise task was also incorporated to evaluate skill transfer. Subjects were evaluated using the Global Rating Scale (GRS), the 14-point arthroscopic checklist, and a timer to determine procedural efficiency (time per task). Secondary outcomes focused on objective measures of virtual reality simulator motion analysis. RESULTS: Trainees on both simulators demonstrated a significant improvement (p < 0.05) in arthroscopic skills compared with baseline scores and untrained controls, both in and ex vivo. The virtual reality simulation group consistently outperformed the bench-top model group in the diagnostic arthroscopy crossover tests and in the simulated Cadaveric setup. Furthermore, the virtual reality group demonstrated superior skill transfer in the surprise skill transfer task. CONCLUSIONS: Both high-fidelity and low-fidelity simulation trainings were effective in arthroscopic skill acquisition. High-fidelity virtual reality simulation was superior to bench-top simulation in the acquisition of arthroscopic skills, both in the laboratory and in vivo. Further clinical investigation is needed to interpret the importance of these results.
