Impact of acute stress on psychomotor bimanual performance during a simulated tumor resection task

Bajunaid, Khalid; Mullah, Muhammad Abu Shadeque; Winkler-Schwartz, Alexander; Alotaibi, Fahad E.; Fares, Jawad; Baggiani, Marta; Azarnoush, Hamed; Christie, Sommer; Alzhrani, Gmaan; Marwa, Ibrahim; Sabbagh, Abdulrahman J.; Werthner, Penny; Maestro, Rolando F. Del

doi:10.3171/2015.5.jns15558

Cited by 39 publications

(58 citation statements)

References 32 publications

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“…3,4 Our group has extensive experience in virtual reality surgical simulation; several studies have demonstrated that performance measures obtained from simulation can differentiate technical skills both between and within groups of expertise. [5][6][7][8][9] Given the task complexity and the abundance of data generated during the simulated operation, we hypothesized that machine learning algorithms could identify previously unrecognized performance measures, as well as differentiate participants according to their stage of medical practice.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Identification of Surgical and Operative Factors Associated With Surgical Expertise in Virtual Reality Simulation

et al. 2019

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IMPORTANCE Despite advances in the assessment of technical skills in surgery, a clear understanding of the composites of technical expertise is lacking. Surgical simulation allows for the quantitation of psychomotor skills, generating data sets that can be analyzed using machine learning algorithms. OBJECTIVE To identify surgical and operative factors selected by a machine learning algorithm to accurately classify participants by level of expertise in a virtual reality surgical procedure. DESIGN, SETTING, AND PARTICIPANTS Fifty participants from a single university were recruited between March 1, 2015, and May 31, 2016, to participate in a case series study at McGill University Neurosurgical Simulation and Artificial Intelligence Learning Centre. Data were collected at a single time point and no follow-up data were collected. Individuals were classified a priori as expert (neurosurgery staff), seniors (neurosurgical fellows and senior residents), juniors (neurosurgical junior residents), and medical students, all of whom participated in 250 simulated tumor resections. EXPOSURES All individuals participated in a virtual reality neurosurgical tumor resection scenario. Each scenario was repeated 5 times. MAIN OUTCOMES AND MEASURES Through an iterative process, performance metrics associated with instrument movement and force, resection of tissues, and bleeding generated from the raw simulator data output were selected by K-nearest neighbor, naive Bayes, discriminant analysis, and support vector machine algorithms to most accurately determine group membership. RESULTS A total of 50 individuals (9 women and 41 men; mean [SD] age, 33.6 [9.5] years; 14 neurosurgeons, 4 fellows, 10 senior residents, 10 junior residents, and 12 medical students) participated. Neurosurgeons were in practice between 1 and 25 years, with 9 (64%) involving a predominantly cranial practice. The K-nearest neighbor algorithm had an accuracy of 90% (45 of 50), the naive Bayes algorithm had an accuracy of 84% (42 of 50), the discriminant analysis algorithm had an accuracy of 78% (39 of 50), and the support vector machine algorithm had an accuracy of 76% (38 of 50). The K-nearest neighbor algorithm used 6 performance metrics to classify participants, the naive Bayes algorithm used 9 performance metrics, the discriminant analysis algorithm used 8 performance metrics, and the support vector machine algorithm used 8 performance metrics. Two neurosurgeons, 1 fellow or senior resident, 1 junior resident, and 1 medical student were misclassified. CONCLUSIONS AND RELEVANCE In a virtual reality neurosurgical tumor resection study, a machine learning algorithm successfully classified participants into 4 levels of expertise with 90% accuracy.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Identification of Surgical and Operative Factors Associated With Surgical Expertise in Virtual Reality Simulation

et al. 2019

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“…The clinical consequences of physicians experiencing a stress state are not well explored. Nonetheless, a stress state impairs psychomotor performance, 5,11 decision-making, 28,29 communication, 29 and retrieval of declarative memory. 30 Furthermore, physicians with a high CA for complex situations will perform with significantly impaired nontechnical performance.…”

Section: Discussionmentioning

confidence: 99%

“…1 For the LP procedure, a stress sensation among novice residents might arise because the LP is a complex procedure 2 with both technical and nontechnical aspects, combined with residents' uncertainties for procedural performance 3 and fear of doing harm. 4 The consequences of being in a state of acute mental stress are reduced working memory, decreased psychomotor performance, 5 and impaired performance. 6 These negative effects of acute stress might therefore compromise patient safety.…”

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Opinion and Special Articles: Stress when performing the first lumbar puncture may compromise patient safety

et al. 2018

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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.…”

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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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Impact of acute stress on psychomotor bimanual performance during a simulated tumor resection task

Cited by 39 publications

References 32 publications

Machine Learning Identification of Surgical and Operative Factors Associated With Surgical Expertise in Virtual Reality Simulation

Machine Learning Identification of Surgical and Operative Factors Associated With Surgical Expertise in Virtual Reality Simulation

Opinion and Special Articles: Stress when performing the first lumbar puncture may compromise patient safety

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

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