Automaticity of Force Application During Simulated Brain Tumor Resection: Testing the Fitts and Posner Model

Bugdadi, Abdulgadir; Sawaya, Robin; Olwi, Duaa; Alzhrani, Gmaan; Azarnoush, Hamed; Sabbagh, Abdulrahman J.; Alsideiri, Ghusn; Bajunaid, Khalid; Alotaibi, Fahad E.; Winkler-Schwartz, Alexander; Maestro, Rolando Del

doi:10.1016/j.jsurg.2017.06.018

Cited by 19 publications

(20 citation statements)

References 24 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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“…T he NeuroVR (formerly NeuroTouch, CAE Healthcare) virtual reality neurosurgical simulation platform has allowed us to deconstruct and evaluate the surgical performance of "expert" and "novice" participants carrying out virtual reality brain tumor resections. [2][3][4][5][6][7]11,12,14,21,23 The performance of neurosurgeon, resident, and medical student groups has been studied using metrics developed by our group. These metrics focus on psychomotor skills such as safety, quality, efficiency, and bimanual dexterity.…”

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confidence: 99%

Development of a performance model for virtual reality tumor resections

Sawaya

Alsideiri

Bugdadi

et al. 2019

Journal of Neurosurgery

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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.

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“…Fitts and Posner's motor learning model proves that if a person is trained with intentional training, then they will progress through the phase of autonomous motor ability. This view is supported by (Bugdadi, 2018) Based on the diagram below, it shows that these three values are also in the range 0 to 1, and it corresponds to a fuzzy number (Ragin, 2007).…”

Section: Theorymentioning

confidence: 94%

“…The framework of this research theory is based on a literature review on sexuality education, sexuality education models, teaching and learning theories, and pedagogical models used in the study. The theoretical framework for this study is a combination of theories and models; Theories underlying Sigmund Freud's Psychosocial Theory (the concept of personality structure) and Maslow's Theory of Needs (1943) (Schmidt et al, 2015); Curriculum Model (Tyler, 1949); Fitts and Posner Model 1972 (Bugdadi et al, 2018); Sex Cycle Models (Dalley, 2012);and SIM Training Models (Milano & Ullius, 1998).…”

Section: Theorymentioning

confidence: 99%

Use of Delphi Fuzzy Technique in Selection of Key Components of Self-Defense Training for Preschool Child Sex Education

Eshak¹,

Zain²,

Hashim³

2020

IJARPED

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This article discusses the use of Fuzzy Delphi in defining key components in Self-Defense Training for preschool children. Because the scale of key components has been chosen to provide martial arts training to preschool children, the selection of these components can be seen as a process of decision making by several experts in the fields of early childhood education, martial arts, medicine-pediatrics, and counseling. This study aims to provide a systematic approach to component selection and key component sequencing, in which there are two phases of the procedure. The first stage uses the Delphi Fuzzy Method to obtain expert consensus on the components of martial arts training. In the second stage, the process is used to determine the importance of each of these criteria as a key title that can be measured from the positioning of the component. The results show that all the elements (items) for the main components of Self-Defense Training meet the threshold value (less than 0.2), and the approval rate of expert groups is over 75%. The main components of Self-Defense Training include family relationship activities, relationships with outsiders, emotional and social health management activities, physical & reproductive health, and safety activities. These findings indicate that all of the key components are necessary to establish a martial arts education based on Self-Defense for preschoolers based on expert consensus.

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Automaticity of Force Application During Simulated Brain Tumor Resection: Testing the Fitts and Posner Model

Cited by 19 publications

References 24 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

Development of a performance model for virtual reality tumor resections

Use of Delphi Fuzzy Technique in Selection of Key Components of Self-Defense Training for Preschool Child Sex Education

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