Proficiency Performance Benchmarks for Removal of Simulated Brain Tumors Using a Virtual Reality Simulator NeuroTouch

Alzhrani, Gmaan; Alotaibi, Fahad E.; Azarnoush, Hamed; Winkler-Schwartz, Alexander; Sabbagh, Abdulrahman J.; Bajunaid, Khalid; Lajoie, Susanne P.; Maestro, Rolando F. Del

doi:10.1016/j.jsurg.2014.12.014

Cited by 57 publications

(63 citation statements)

References 29 publications

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“…Simulation in cranial neurosurgery has acquired much attention with the NeuroTouch simulator 58,72,92. The Neuro-Touch training tool was developed by the National Research Council in Canada and is a simulator built around a stereoscope, which incorporates bimanual tools that provide haptic feedback and a real-time computer-generated virtual tissue that reacts to manipulation.…”

Section: Simulation In Neurosurgerymentioning

confidence: 99%

Simulation training in neurosurgery: advances in education and practice

Konakondla

Fong

Schirmer

2017

AMEP

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The current simulation technology used for neurosurgical training leaves much to be desired. Significant efforts are thoroughly exhausted in hopes of developing simulations that translate to give learners the “real-life” feel. Though a respectable goal, this may not be necessary as the application for simulation in neurosurgical training may be most useful in early learners. The ultimate uniformly agreeable endpoint of improved outcome and patient safety drives these investments. We explore the development, availability, educational taskforces, cost burdens and the simulation advancements in neurosurgical training. The technologies can be directed at achieving early resident milestones placed by the Accreditation Council for Graduate Medical Education. We discuss various aspects of neurosurgery disciplines with specific technologic advances of simulation software. An overview of the scholarly landscape of the recent publications in the realm of medical simulation and virtual reality pertaining to neurologic surgery is provided. We analyze concurrent concept overlap between PubMed headings and provide a graphical overview of the associations between these terms.

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Section: Simulation In Neurosurgerymentioning

confidence: 99%

Simulation training in neurosurgery: advances in education and practice

Konakondla

Fong

Schirmer

2017

AMEP

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“…It seemed reasonable to define a skilled and novice (less skilled) group based on the required skill set needed for the 6 scenarios studied. 9,25 We applied 4 different supervised machine learning classifiers to the data set involving these participants. Our results demonstrate that all 4 classifier distinguished skilled and novice groups with equal error rates as low as 8.3% indicating the usefulness of classifiers in differentiating participants doing virtual reality procedures.…”

Section: Differentiating Skilled and Novice Performancementioning

confidence: 99%

“…An operator's performance metric(s) can be compared with previously defined proficiency benchmarks and that operator is placed into 1 of 2 or more groups with specific levels of psychomotor expertise. 24,25 Neurosurgical tasks are complicated, involving multiple cognitive processes and psychomotor skills, and larger sets of more complex and interacting metrics may be required to differentiate groups.…”

Section: Introductionmentioning

confidence: 99%

Machine learning distinguishes neurosurgical skill levels in a virtual reality tumor resection task

Siyar

Azarnoush

Rashidi

et al. 2020

Med Biol Eng Comput

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Background: Virtual reality simulators and machine learning have the potential to augment understanding, assessment and training of psychomotor performance in neurosurgery residents.Objective: This study outlines the first application of machine learning to distinguish "skilled" and "novice" psychomotor performance during a virtual reality neurosurgical task.Methods: Twenty-three neurosurgeons and senior neurosurgery residents comprising the "skilled" group and 92 junior neurosurgery residents and medical students the "novice" group. The task involved removing a series of virtual brain tumors without causing injury to surrounding tissue.Over 100 features were extracted and 68 selected using t-test analysis. These features were provided to 4 classifiers: K-Nearest Neighbors, Parzen Window, Support Vector Machine, and Fuzzy K-Nearest Neighbors. Equal Error Rate was used to assess classifier performance. Results:Ratios of train set size to test set size from 10% to 90% and 5 to 30 features, chosen by the forward feature selection algorithm, were employed. A working point of 50% train to test set size ratio and 15 features resulted in an equal error rates as low as 8.3% using the Fuzzy K-Nearest Neighbors classifier. Conclusion:Machine learning may be one component helping realign the traditional apprenticeship educational paradigm to a more objective model based on proven performance standards.

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“…The previously described NeuroTouch (now known as NeuroVR) platform was used to conduct this study. [1][2][3][4][5][6][7]10,11,14,16,20,26 Tumor removal was accomplished with a simulated virtual ultrasonic aspirator held in the participant's dominant hand (Fig. 1A).…”

Section: Neurovr Simulatormentioning

confidence: 99%

“…rics have been used in a series of studies to demonstrate face, content, and construct validity of the NeuroTouch/ NeuroVR platform. [1][2][3][4][5][6][7]14 The concept of the force pyramid was introduced as one of the Tier 3 metrics with the ability to display and assess the forces applied by any virtual instrument in any tumor region during the resection of 3D tumors. 6 A number of studies have equipped neurosurgery tools with force sensors or used robotic tools to make force measurements.…”

mentioning

confidence: 99%

The force pyramid: a spatial analysis of force application during virtual reality brain tumor resection

Azarnoush

Siar

Sawaya

et al. 2017

Journal of Neurosurgery

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OBJECTIVE Virtual reality simulators allow development of novel methods to analyze neurosurgical performance. The concept of a force pyramid is introduced as a Tier 3 metric with the ability to provide visual and spatial analysis of 3D force application by any instrument used during simulated tumor resection. This study was designed to answer 3 questions: 1) Do study groups have distinct force pyramids? 2) Do handedness and ergonomics influence force pyramid structure? 3) Are force pyramids dependent on the visual and haptic characteristics of simulated tumors? METHODS Using a virtual reality simulator, NeuroVR (formerly NeuroTouch), ultrasonic aspirator force application was continually assessed during resection of simulated brain tumors by neurosurgeons, residents, and medical students. The participants performed simulated resections of 18 simulated brain tumors with different visual and haptic characteristics. The raw data, namely, coordinates of the instrument tip as well as contact force values, were collected by the simulator. To provide a visual and qualitative spatial analysis of forces, the authors created a graph, called a force pyramid, representing force sum along the z-coordinate for different xy coordinates of the tool tip. RESULTS Sixteen neurosurgeons, 15 residents, and 84 medical students participated in the study. Neurosurgeon, resident and medical student groups displayed easily distinguishable 3D "force pyramid fingerprints." Neurosurgeons had the lowest force pyramids, indicating application of the lowest forces, followed by resident and medical student groups. Handedness, ergonomics, and visual and haptic tumor characteristics resulted in distinct well-defined 3D force pyramid patterns. CONCLUSIONS Force pyramid fingerprints provide 3D spatial assessment displays of instrument force application during simulated tumor resection. Neurosurgeon force utilization and ergonomic data form a basis for understanding and modulating resident force application and improving patient safety during tumor resection.

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Proficiency Performance Benchmarks for Removal of Simulated Brain Tumors Using a Virtual Reality Simulator NeuroTouch

Cited by 57 publications

References 29 publications

Simulation training in neurosurgery: advances in education and practice

Simulation training in neurosurgery: advances in education and practice

Machine learning distinguishes neurosurgical skill levels in a virtual reality tumor resection task

The force pyramid: a spatial analysis of force application during virtual reality brain tumor resection

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