Gmaan AlZhrani1, Fahad Alotaibi2, Hamed Azarnoush3, Alexander Winkler-Schwartz4, Abdulrahman Sabbagh2, Khalid Bajunaid5, Susanne P Lajoie6, Rolando F Del Maestro4. 1. Neurosurgical Simulation Research Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada; National Neuroscience Institute, King Fahad Medical City, Riyadh, Saudi Arabia. Electronic address: gmaan.al-zhrani@mail.mcgill.ca. 2. Neurosurgical Simulation Research Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada; National Neuroscience Institute, King Fahad Medical City, Riyadh, Saudi Arabia. 3. Neurosurgical Simulation Research Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada; Department of Biomedical Engineering, Tehran Polytechnic, Tehran, Iran. 4. Neurosurgical Simulation Research Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada. 5. Neurosurgical Simulation Research Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada; Division of Neurosurgery, King Abdulaziz University, Jeddah, Saudi Arabia. 6. Department of Educational and Counseling Psychology, McGill University, Montreal, Canada.
Abstract
OBJECTIVE: Assessment of neurosurgical technical skills involved in the resection of cerebral tumors in operative environments is complex. Educators emphasize the need to develop and use objective and meaningful assessment tools that are reliable and valid for assessing trainees' progress in acquiring surgical skills. The purpose of this study was to develop proficiency performance benchmarks for a newly proposed set of objective measures (metrics) of neurosurgical technical skills performance during simulated brain tumor resection using a new virtual reality simulator (NeuroTouch). DESIGN: Each participant performed the resection of 18 simulated brain tumors of different complexity using the NeuroTouch platform. Surgical performance was computed using Tier 1 and Tier 2 metrics derived from NeuroTouch simulator data consisting of (1) safety metrics, including (a) volume of surrounding simulated normal brain tissue removed, (b) sum of forces utilized, and (c) maximum force applied during tumor resection; (2) quality of operation metric, which involved the percentage of tumor removed; and (3) efficiency metrics, including (a) instrument total tip path lengths and (b) frequency of pedal activation. SETTING: All studies were conducted in the Neurosurgical Simulation Research Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada. PARTICIPANTS: A total of 33 participants were recruited, including 17 experts (board-certified neurosurgeons) and 16 novices (7 senior and 9 junior neurosurgery residents). RESULTS: The results demonstrated that "expert" neurosurgeons resected less surrounding simulated normal brain tissue and less tumor tissue than residents. These data are consistent with the concept that "experts" focused more on safety of the surgical procedure compared with novices. By analyzing experts' neurosurgical technical skills performance on these different metrics, we were able to establish benchmarks for goal proficiency performance training of neurosurgery residents. CONCLUSION: This study furthers our understanding of expert neurosurgical performance during the resection of simulated virtual reality tumors and provides neurosurgical trainees with predefined proficiency performance benchmarks designed to maximize the learning of specific surgical technical skills.
OBJECTIVE: Assessment of neurosurgical technical skills involved in the resection of cerebral tumors in operative environments is complex. Educators emphasize the need to develop and use objective and meaningful assessment tools that are reliable and valid for assessing trainees' progress in acquiring surgical skills. The purpose of this study was to develop proficiency performance benchmarks for a newly proposed set of objective measures (metrics) of neurosurgical technical skills performance during simulated brain tumor resection using a new virtual reality simulator (NeuroTouch). DESIGN: Each participant performed the resection of 18 simulated brain tumors of different complexity using the NeuroTouch platform. Surgical performance was computed using Tier 1 and Tier 2 metrics derived from NeuroTouch simulator data consisting of (1) safety metrics, including (a) volume of surrounding simulated normal brain tissue removed, (b) sum of forces utilized, and (c) maximum force applied during tumor resection; (2) quality of operation metric, which involved the percentage of tumor removed; and (3) efficiency metrics, including (a) instrument total tip path lengths and (b) frequency of pedal activation. SETTING: All studies were conducted in the Neurosurgical Simulation Research Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada. PARTICIPANTS: A total of 33 participants were recruited, including 17 experts (board-certified neurosurgeons) and 16 novices (7 senior and 9 junior neurosurgery residents). RESULTS: The results demonstrated that "expert" neurosurgeons resected less surrounding simulated normal brain tissue and less tumor tissue than residents. These data are consistent with the concept that "experts" focused more on safety of the surgical procedure compared with novices. By analyzing experts' neurosurgical technical skills performance on these different metrics, we were able to establish benchmarks for goal proficiency performance training of neurosurgery residents. CONCLUSION: This study furthers our understanding of expert neurosurgical performance during the resection of simulated virtual reality tumors and provides neurosurgical trainees with predefined proficiency performance benchmarks designed to maximize the learning of specific surgical technical skills.
Authors: Recai Yilmaz; Alexander Winkler-Schwartz; Nykan Mirchi; Aiden Reich; Sommer Christie; Dan Huy Tran; Nicole Ledwos; Ali M Fazlollahi; Carlo Santaguida; Abdulrahman J Sabbagh; Khalid Bajunaid; Rolando Del Maestro Journal: NPJ Digit Med Date: 2022-04-26
Authors: Alessandro Iop; Victor Gabriel El-Hajj; Maria Gharios; Andrea de Giorgio; Fabio Marco Monetti; Erik Edström; Adrian Elmi-Terander; Mario Romero Journal: Sensors (Basel) Date: 2022-08-14 Impact factor: 3.847