Development of a performance model for virtual reality tumor resections

Restricted access

OBJECTIVE

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.

METHODS

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).

RESULTS

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.

CONCLUSIONS

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. robin.sawaya@mail.mcgill.ca.

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.

Headings

Figures

  • View in gallery

    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.

  • View in gallery

    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.

  • View in gallery

    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.

  • View in gallery

    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.

  • View in gallery

    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.

  • View in gallery

    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.

References

1

Adams BDGrosland NMMurphy DMMcCullough M: Impact of impaired wrist motion on hand and upper-extremity performance. J Hand Surg Am 28:8989032003

2

Alotaibi FEAlZhrani GAMullah MASabbagh AJAzarnoush HWinkler-Schwartz A: Assessing bimanual performance in brain tumor resection with NeuroTouch, a virtual reality simulator. Neurosurgery 11 (Suppl 2):89982015

3

Alotaibi FEAlZhrani GASabbagh AJAzarnoush HWinkler-Schwartz ADel Maestro RF: Neurosurgical assessment of metrics including judgment and dexterity using the virtual reality simulator NeuroTouch (NAJD metrics). Surg Innov 22:6366422015

4

AlZhrani GAlotaibi FAzarnoush HWinkler-Schwartz ASabbagh ABajunaid K: Proficiency performance benchmarks for removal of simulated brain tumors using a virtual reality simulator NeuroTouch. J Surg Educ 72:6856962015

5

Azarnoush HAlzhrani GWinkler-Schwartz AAlotaibi FGelinas-Phaneuf NPazos V: Neurosurgical virtual reality simulation metrics to assess psychomotor skills during brain tumor resection. Int J CARS 10:6036182015

6

Azarnoush HSiar SSawaya RZhrani GAWinkler-Schwartz AAlotaibi FE: The force pyramid: a spatial analysis of force application during virtual reality brain tumor resection. J Neurosurg 127:1711812017

7

Bajunaid KMullah MAWinkler-Schwartz AAlotaibi FEFares JBaggiani M: Impact of acute stress on psychomotor bimanual performance during a simulated tumor resection task. J Neurosurg 126:71802017

8

Berguer R: The application of ergonomics in the work environment of general surgeons. Rev Environ Health 12:991061997

9

Berguer R: Surgical technology and the ergonomics of laparoscopic instruments. Surg Endosc 12:4584621998

10

Berguer RForkey DLSmith WD: Ergonomic problems associated with laparoscopic surgery. Surg Endosc 13:4664681999

11

Bugdadi ASawaya ROlwi DAl-Zhrani GAzarnoush HSabbagh A: Automaticity of force application during simulated brain tumor resection: testing the Fitts and Posner model. J Surg Educ 75:1041152018

12

Delorme SLaroche DDiRaddo RDel Maestro RF: NeuroTouch: a physics-based virtual simulator for cranial microneurosurgery training. Neurosurgery 71 (1 Suppl Operative):32422012

13

Gelberman RHSzabo RMWilliamson RVHargens ARYaru NCMinteer-Convery MA: Tissue pressure threshold for peripheral nerve viability. Clin Orthop Relat Res (178):2852911983

14

Gélinas-Phaneuf NChoudhury NAl-Habib ARCabral ANadeau EMora V: Assessing performance in brain tumor resection using a novel virtual reality simulator. Int J CARS 9:192014

15

Gélinas-Phaneuf NDel Maestro RF: Surgical expertise in neurosurgery: integrating theory into practice. Neurosurgery 73 (Suppl 1):30382013

16

Hanna GBElamass MCuschieri A: Ergonomics of hand-assisted laparoscopic surgery. Semin Laparosc Surg 8:92952001

17

Keir PJBach JMRempel DM: Effects of finger posture on carpal tunnel pressure during wrist motion. J Hand Surg Am 23:100410091998

18

Lee SH: Pianists’ hand ergonomics and touch control. Med Probl Perform Art 5:72781990

19

Lemos JDHernandez AMSoto-Romero G: An instrumented glove to assess manual dexterity in simulation-based neurosurgical education. Sensors (Basel) 17:9882017

20

Luchetti RSchoenhuber RAlfarano MDeluca SDe Cicco GLandi A: Carpal tunnel syndrome: correlations between pressure measurement and intraoperative electrophysiological nerve study. Muscle Nerve 13:116411681990

21

Sawaya RBugdadi AAzarnoush HWinkler-Schwartz AAlotaibi FEBajunaid K: Virtual reality tumor resection: the force pyramid approach. Oper Neurosurg (Hagerstown) 14:6866962017

22

Visser BDe Looze MDe Graaff MVan Dieën J: Effects of precision demands and mental pressure on muscle activation and hand forces in computer mouse tasks. Ergonomics 47:2022172004

23

Winkler-Schwartz ABajunaid KMullah MASMarwa IAlotaibi FEFares J: Bimanual psychomotor performance in neurosurgical resident applicants assessed using NeuroTouch, a virtual reality simulator. J Surg Educ 73:9429532016

TrendMD

Metrics

Metrics

All Time Past Year Past 30 Days
Abstract Views 171 171 47
Full Text Views 251 251 23
PDF Downloads 74 74 6
EPUB Downloads 0 0 0

PubMed

Google Scholar