Development of a performance model for virtual reality tumor resections

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Previous work from the authors has shown that hand ergonomics plays an important role in surgical psychomotor performance during virtual reality brain tumor resections. In the current study they propose a hypothetical model that integrates the human and task factors at play during simulated brain tumor resections to better understand the hand ergonomics needed for optimal safety and efficiency. They hypothesize that 1) experts (neurosurgeons), compared to novices (residents and medical students), spend a greater proportion of their time in direct contact with critical tumor areas; 2) hand ergonomic conditions (most favorable to unfavorable) prompt participants to adapt in order to optimize tumor resection; and 3) hand ergonomic adaptation is acquired with increasing expertise.


In an earlier study, experts (neurosurgeons) and novices (residents and medical students) were instructed to resect simulated brain tumors on the NeuroVR (formerly NeuroTouch) virtual reality neurosurgical simulation platform. For the present study, the simulated tumors were divided into four quadrants (Q1 to Q4) to assess hand ergonomics at various levels of difficulty. The spatial distribution of time expended, force applied, and tumor volume removed was analyzed for each participant group (total of 22 participants).


Neurosurgeons spent a significantly greater percentage of their time in direct contact with critical tumor areas. Under the favorable hand ergonomic conditions of Q1 and Q3, neurosurgeons and senior residents spent significantly more time in Q1 than in Q3. Although forces applied in these quadrants were similar, neurosurgeons, having spent more time in Q1, removed significantly more tumor in Q1 than in Q3. In a comparison of the most favorable (Q2) to unfavorable (Q4) hand ergonomic conditions, neurosurgeons adapted the forces applied in each quadrant to resect similar tumor volumes. Differences between Q2 and Q4 were emphasized in measures of force applied per second, tumor volume removed per second, and tumor volume removed per unit of force applied. In contrast, the hand ergonomics of medical students did not vary across quadrants, indicating the existence of an “adaptive capacity” in neurosurgeons.


The study results confirm the experts’ (neurosurgeons) greater capacity to adapt their hand ergonomics during simulated neurosurgical tasks. The proposed hypothetical model integrates the study findings with various human and task factors that highlight the importance of learning in the acquisition of hand ergonomic adaptation.

ABBREVIATIONS PGY = postgraduate year; Q1, . . . , Q4 = quadrant 1, . . . , quadrant 4.

Article Information

Correspondence Robin Sawaya: Neurosurgical Simulation Research and Training Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.

INCLUDE WHEN CITING Published online August 3, 2018; DOI: 10.3171/2018.2.JNS172327.

Disclosures The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

© AANS, except where prohibited by US copyright law.



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    Study design. A: Instrument setup. To carry out the resection, participants held a simulated aspirator in their dominant hand (right), and to control the bleeding, they held a sucker in their nondominant hand (left). B: The simulated tumors (top view) presented to the participants were identical in color (glioma-like) and stiffness (Young’s modulus 9 kPa) and were embedded in a white matter–like background (Young’s modulus 3 kPa). C: Surgical field areas (side view). Cross-section of a tumor presented to participants. Three surgical field areas can be identified: area A corresponds to all components that are not the tumor; area B corresponds to tumor above the surface of the brain; area C corresponds to tumor below the surface of the brain. D: Tumors are divided into four quadrants (top view) presented in a counter-clockwise fashion. These divisions extend through the depth of the tumor. Figure is available in color online only.

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    Time expended per area. The percent of the total time spent in each area is shown for all participant groups: neurosurgeons (NS), n = 6; senior residents (SR), n = 5; junior residents (JR), n = 6; medical students (MS), n = 5. *p < 0.05 or **p < 0.01. Figure is available in color online only.

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    Simple metrics. The total time expended (s), total force applied (N), and total volume of tumor removed (cc) are calculated per quadrant for each group: neurosurgeons (NS), n = 6; senior residents (SR), n = 5; junior residents (JR), n = 6; medical students (MS), n = 5. *p < 0.05 or **p < 0.01. Figure is available in color online only.

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    Advanced metrics. The mean force applied per second (N/s), mean volume of tumor removed per second (cc/s), and mean tumor volume removed per unit of force applied (cc/N) are calculated per quadrant for each group: neurosurgeons (NS), n = 6; senior residents (SR), n = 5; junior residents (JR), n = 6; medical students (MS), n = 5. *p < 0.05 or **p < 0.01. Figure is available in color online only.

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    The 95% confidence ellipses show the spread and relationship between the mean force applied per second (N/s) and the mean volume of tumor removed per second (cc/s) for Q2 and Q4, the quadrants that are most significantly different for each group: neurosurgeons (NS), n = 6; senior residents (SR), n = 5; junior residents (JR), n = 6; medical students (MS), n = 5. There is minimal overlap between the two quadrants for neurosurgeons in comparison to those for the other groups. Figure is available in color online only.

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    Hypothetical model of surgical performance in virtual reality brain tumor resections. Diagram representing the effect of various human and task factors, which, after integration and adaptation, produce specific hand ergonomics and result in a surgical performance focused on the overall safety and efficiency of the procedure.



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