1
|
Sayrafizadeh N, Ledwos N, Husain MI, Castle DJ. Aggressive behaviours associated with MDMA and psychedelics: a narrative review. Acta Neuropsychiatr 2024:1-13. [PMID: 38329106 DOI: 10.1017/neu.2024.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
OBJECTIVE Monoamine neurotransmitters play a role in aggression, especially when altered by illicit substances. However, some literature suggests that not all illicit substances may lead to aggression, notably psychedelics. This narrative review investigates the associations between serotonergic psychedelics and MDMA on aggressive behaviour. METHODS PubMed and PsycINFO were searched for original, peer-reviewed articles evaluating the effects of serotonergic psychedelics and 3,4-methyl enedioxy methamphetamine (MDMA) on violent and aggressive behaviour using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS After removing duplicates, a total of 555 articles were screened, with 16 meeting the inclusion criteria. One additional article was obtained through reference screening bringing the total to 17 articles. Of these 17 articles, 14 studies focused on MDMA and three on serotonergic psychedelics. Findings were mixed, with some results demonstrating increased aggression following psychedelics and others suggesting protective effects. Limitations in the current literature include varied definitions of psychedelics, lack of standardised objective outcome measures and failure to control for confounding. CONCLUSION As psychedelic research continues to expand, further assessment on the effects of serotonergic psychedelics and MDMA on aggressive behaviour is required.
Collapse
Affiliation(s)
- Negar Sayrafizadeh
- Centre for Complex Interventions, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Nicole Ledwos
- Centre for Complex Interventions, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - M Ishrat Husain
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - David J Castle
- Centre for Complex Interventions, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
2
|
Bakhaidar M, Alsayegh A, Yilmaz R, Fazlollahi AM, Ledwos N, Mirchi N, Winkler-Schwartz A, Luo L, Del Maestro RF. Performance in a Simulated Virtual Reality Anterior Cervical Discectomy and Fusion Task: Disc Residual, Rate of Removal, and Efficiency Analyses. Oper Neurosurg (Hagerstown) 2023; 25:e196-e205. [PMID: 37441799 DOI: 10.1227/ons.0000000000000813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 05/05/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Anterior cervical discectomy and fusion (ACDF) is among the most common spine procedures. The Sim-Ortho virtual reality simulator platform contains a validated ACDF simulated task for performance assessment. This study aims to develop a methodology to extract three-dimensional data and reconstruct and quantitate specific simulated disc tissues to generate novel metrics to analyze performance metrics of skilled and less skilled participants. METHODS We used open-source platforms to develop a methodology to extract three-dimensional information from ACDF simulation data. Metrics generated included, efficiency index, disc volumes removed from defined regions, and rate of tissue removal from superficial, central, and deep disc regions. A pilot study was performed to assess the utility of this methodology to assess expertise during the ACDF simulated procedure. RESULTS The system outlined, extracts data allowing the development of a methodology which accurately reconstructs and quantitates 3-dimensional disc volumes. In the pilot study, data sets from 27 participants, divided into postresident, resident, and medical student groups, allowed assessment of multiple novel metrics, including efficiency index (surgical time spent in actively removing disc), where the postresident group spent 61.8% of their time compared with 53% and 30.2% for the resident and medical student groups, respectively ( P = .01). During the annulotomy component, the postresident group removed 47.4% more disc than the resident groups and 102% more than the medical student groups ( P = .03). CONCLUSION The methodology developed in this study generates novel surgical procedural metrics from 3-dimensional data generated by virtual reality simulators and can be used to assess surgical performance.
Collapse
Affiliation(s)
- Mohamad Bakhaidar
- Department of Neurology and Neurosurgery, Neurosurgical Simulation and Artificial Intelligence Learning Centre, Montreal Neurological Hospital and Institute, McGill University, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, Montreal Neurological Hospital and Institute, McGill University, Montreal, Quebec, Canada
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmad Alsayegh
- Department of Neurology and Neurosurgery, Neurosurgical Simulation and Artificial Intelligence Learning Centre, Montreal Neurological Hospital and Institute, McGill University, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, Montreal Neurological Hospital and Institute, McGill University, Montreal, Quebec, Canada
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Recai Yilmaz
- Department of Neurology and Neurosurgery, Neurosurgical Simulation and Artificial Intelligence Learning Centre, Montreal Neurological Hospital and Institute, McGill University, Montreal, Quebec, Canada
| | - Ali M Fazlollahi
- Department of Neurology and Neurosurgery, Neurosurgical Simulation and Artificial Intelligence Learning Centre, Montreal Neurological Hospital and Institute, McGill University, Montreal, Quebec, Canada
| | - Nicole Ledwos
- Department of Neurology and Neurosurgery, Neurosurgical Simulation and Artificial Intelligence Learning Centre, Montreal Neurological Hospital and Institute, McGill University, Montreal, Quebec, Canada
| | - Nykan Mirchi
- Department of Neurology and Neurosurgery, Neurosurgical Simulation and Artificial Intelligence Learning Centre, Montreal Neurological Hospital and Institute, McGill University, Montreal, Quebec, Canada
| | - Alexander Winkler-Schwartz
- Department of Neurology and Neurosurgery, Neurosurgical Simulation and Artificial Intelligence Learning Centre, Montreal Neurological Hospital and Institute, McGill University, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, Montreal Neurological Hospital and Institute, McGill University, Montreal, Quebec, Canada
| | - Lucy Luo
- Department of Neurology and Neurosurgery, Neurosurgical Simulation and Artificial Intelligence Learning Centre, Montreal Neurological Hospital and Institute, McGill University, Montreal, Quebec, Canada
- Department of Orthopedic Surgery, McGill University Health Centre, Montreal, Quebec, Canada
| | - Rolando F Del Maestro
- Department of Neurology and Neurosurgery, Neurosurgical Simulation and Artificial Intelligence Learning Centre, Montreal Neurological Hospital and Institute, McGill University, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, Montreal Neurological Hospital and Institute, McGill University, Montreal, Quebec, Canada
| |
Collapse
|
3
|
Fazlollahi AM, Yilmaz R, Winkler-Schwartz A, Mirchi N, Ledwos N, Bakhaidar M, Alsayegh A, Del Maestro RF. AI in Surgical Curriculum Design and Unintended Outcomes for Technical Competencies in Simulation Training. JAMA Netw Open 2023; 6:e2334658. [PMID: 37725373 PMCID: PMC10509729 DOI: 10.1001/jamanetworkopen.2023.34658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 08/06/2023] [Indexed: 09/21/2023] Open
Abstract
Importance To better elucidate the role of artificial intelligence (AI) in surgical skills training requires investigations in the potential existence of a hidden curriculum. Objective To assess the pedagogical value of AI-selected technical competencies and their extended effects in surgical simulation training. Design, Setting, and Participants This cohort study was a follow-up of a randomized clinical trial conducted at the Neurosurgical Simulation and Artificial Intelligence Learning Centre at the Montreal Neurological Institute, McGill University, Montreal, Canada. Surgical performance metrics of medical students exposed to an AI-enhanced training curriculum were compared with a control group of participants who received no feedback and with expert benchmarks. Cross-sectional data were collected from January to April 2021 from medical students and from March 2015 to May 2016 from experts. This follow-up secondary analysis was conducted from June to September 2022. Participants included medical students (undergraduate year 0-2) in the intervention cohorts and neurosurgeons to establish expertise benchmarks. Exposure Performance assessment and personalized feedback by an intelligent tutor on 4 AI-selected learning objectives during simulation training. Main Outcomes and Measures Outcomes of interest were unintended performance outcomes, measured by significant within-participant difference from baseline in 270 performance metrics in the intervention cohort that was not observed in the control cohort. Results A total of 46 medical students (median [range] age, 22 [18-27] years; 27 [59%] women) and 14 surgeons (median [range] age, 45 [35-59] years; 14 [100%] men) were included in this study, and no participant was lost to follow-up. Feedback on 4 AI-selected technical competencies was associated with additional performance change in 32 metrics over the entire procedure and 20 metrics during tumor removal that was not observed in the control group. Participants exposed to the AI-enhanced curriculum demonstrated significant improvement in safety metrics, such as reducing the rate of healthy tissue removal (mean difference, -7.05 × 10-5 [95% CI, -1.09 × 10-4 to -3.14 × 10-5] mm3 per 20 ms; P < .001) and maintaining a focused bimanual control of the operative field (mean difference in maximum instrument divergence, -4.99 [95% CI, -8.48 to -1.49] mm, P = .006) compared with the control group. However, negative unintended effects were also observed. These included a significantly lower velocity and acceleration in the dominant hand (velocity: mean difference, -0.13 [95% CI, -0.17 to -0.09] mm per 20 ms; P < .001; acceleration: mean difference, -2.25 × 10-2 [95% CI, -3.20 × 10-2 to -1.31 × 10-2] mm per 20 ms2; P < .001) and a significant reduction in the rate of tumor removal (mean difference, -4.85 × 10-5 [95% CI, -7.22 × 10-5 to -2.48 × 10-5] mm3 per 20 ms; P < .001) compared with control. These unintended outcomes diverged students' movement and efficiency performance metrics away from the expertise benchmarks. Conclusions and Relevance In this cohort study of medical students, an AI-enhanced curriculum for bimanual surgical skills resulted in unintended changes that improved performance in safety but negatively affected some efficiency metrics. Incorporating AI in course design requires ongoing assessment to maintain transparency and foster evidence-based learning objectives.
Collapse
Affiliation(s)
- Ali M. Fazlollahi
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
| | - Recai Yilmaz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
| | - Alexander Winkler-Schwartz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Nykan Mirchi
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Nicole Ledwos
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Mohamad Bakhaidar
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmad Alsayegh
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Rolando F. Del Maestro
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
| |
Collapse
|
4
|
Husain MI, Blumberger DM, Castle DJ, Ledwos N, Fellows E, Jones BDM, Ortiz A, Kloiber S, Wang W, Rosenblat JD, Mulsant BH. Psilocybin for treatment-resistant depression without psychedelic effects: study protocol for a 4-week, double-blind, proof-of-concept randomised controlled trial. BJPsych Open 2023; 9:e134. [PMID: 37489299 PMCID: PMC10375870 DOI: 10.1192/bjo.2023.535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/26/2023] Open
Abstract
BACKGROUND Randomised controlled trials (RCTs) of psilocybin have reported large antidepressant effects in adults with major depressive disorder and treatment-resistant depression (TRD). Given psilocybin's psychedelic effects, all published studies have included psychological support. These effects depend on serotonin 2A (5-HT2A) receptor activation, which can be blocked by 5-HT2A receptor antagonists like ketanserin or risperidone. In an animal model of depression, ketanserin followed by psilocybin had similar symptomatic effects as psilocybin alone. AIMS To conduct a proof-of-concept RCT to (a) establish feasibility and tolerability of combining psilocybin and risperidone in adults with TRD, (b) show that this combination blocks the psychedelic effects of psilocybin and (c) provide pilot data on the antidepressant effect of this combination (compared with psilocybin alone). METHOD In a 4-week, three-arm, 'double dummy' trial, 60 adults with TRD will be randomised to psilocybin 25 mg plus risperidone 1 mg, psilocybin 25 mg plus placebo, or placebo plus risperidone 1 mg. All participants will receive 12 h of manualised psychotherapy. Measures of feasibility will include recruitment and retention rates; tolerability and safety will be assessed by rates of drop-out attributed to adverse events and rates of serious adverse events. The 5-Dimensional Altered States of Consciousness Rating Scale will be a secondary outcome measure. RESULTS This trial will advance the understanding of psilocybin's mechanism of antidepressant action. CONCLUSIONS This line of research could increase acceptability and access to psilocybin as a novel treatment for TRD without the need for a psychedelic experience and continuous monitoring.
Collapse
Affiliation(s)
- Muhammad Ishrat Husain
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Canada; and Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Daniel M Blumberger
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada; and Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Canada
| | - David J Castle
- Department of Psychiatry, University of Tasmania, Australia
| | - Nicole Ledwos
- General Adult Psychiatry and Health Systems Division, Centre for Addiction and Mental Health, Canada
| | - Elise Fellows
- General Adult Psychiatry and Health Systems Division, Centre for Addiction and Mental Health, Canada; and Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Brett D M Jones
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Canada; and Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Abigail Ortiz
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Canada; and Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Stefan Kloiber
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Canada; and Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Wei Wang
- Centre for Complex Interventions, Centre for Addiction and Mental Health, Canada; and College of Public Health, University of South Florida, USA
| | - Joshua D Rosenblat
- Mood Disorder Psychopharmacology Program, Poul Hansen Depression Centre, Unit University Health Network, Canada
| | - Benoit H Mulsant
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Canada; and Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| |
Collapse
|
5
|
Ledwos N, Andreiev AI, Costa T, Chopra N, George TP. Successful treatment of dextromethorphan use disorder with combined naltrexone and gabapentin: a case report. Am J Drug Alcohol Abuse 2023; 49:266-267. [PMID: 36920953 DOI: 10.1080/00952990.2023.2175323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Affiliation(s)
- Nicole Ledwos
- Centre for Complex Interventions, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Aurelia I Andreiev
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Tianna Costa
- Pharmacy Services, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Nitin Chopra
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Tony P George
- Centre for Complex Interventions, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada.,Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
6
|
Ledwos N, Rodas JD, Husain MI, Feusner JD, Castle DJ. Therapeutic uses of psychedelics for eating disorders and body dysmorphic disorder. J Psychopharmacol 2023; 37:3-13. [PMID: 36515406 DOI: 10.1177/02698811221140009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND Clinical use of psychedelics has gained considerable attention, with promising benefits across a range of mental disorders. Current pharmacological and psychotherapeutic treatments for body dysmorphic disorder (BDD) and eating disorders (EDs) have limited efficacy. As such, other treatment options such as psychedelic-assisted therapies are being explored in these clinical groups. AIMS This systematic review evaluates evidence related to the therapeutic potential of psychedelics in individuals diagnosed with BDD and EDs. METHODS Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a systematic review of all study designs published to the end of February 2022 that identified changes in ED/BDD symptom severity from psychedelics using validated measures to assess symptom changes. RESULTS Our search detected a total of 372 studies, of which five met inclusion criteria (two exploratory studies, two case reports, and one prospective study). These were included in the data evaluation. Effects of psychedelics on BDD and various ED symptoms were identified mostly through thematic analyses and self-reports. CONCLUSIONS Our findings highlight that more research is needed to determine the safety and efficacy of psychedelics in BDD and EDs and we suggest avenues for future exploration.
Collapse
Affiliation(s)
- Nicole Ledwos
- Centre for Complex Interventions, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Justyne D Rodas
- Centre for Complex Interventions, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - M Ishrat Husain
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Jamie D Feusner
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - David J Castle
- Centre for Complex Interventions, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
7
|
Reich A, Mirchi N, Yilmaz R, Ledwos N, Bissonnette V, Tran DH, Winkler-Schwartz A, Karlik B, Del Maestro RF. Artificial Neural Network Approach to Competency-Based Training Using a Virtual Reality Neurosurgical Simulation. Oper Neurosurg (Hagerstown) 2022; 23:31-39. [PMID: 35726927 DOI: 10.1227/ons.0000000000000173] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 11/08/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND The methodology of assessment and training of surgical skills is evolving to deal with the emergence of competency-based training. Artificial neural networks (ANNs), a branch of artificial intelligence, can use newly generated metrics not only for assessment performance but also to quantitate individual metric importance and provide new insights into surgical expertise. OBJECTIVE To outline the educational utility of using an ANN in the assessment and quantitation of surgical expertise. A virtual reality vertebral osteophyte removal during a simulated surgical spine procedure is used as a model to outline this methodology. METHODS Twenty-one participants performed a simulated anterior cervical diskectomy and fusion on the Sim-Ortho virtual reality simulator. Participants were divided into 3 groups, including 9 postresidents, 5 senior residents, and 7 junior residents. Data were retrieved from the osteophyte removal component of the scenario, which involved using a simulated burr. The data were manipulated to initially generate 83 performance metrics spanning 3 categories (safety, efficiency, and motion) of which only the most relevant metrics were used to train and test the ANN. RESULTS The ANN model was trained on 6 safety metrics to a testing accuracy of 83.3%. The contributions of these performance metrics to expertise were revealed through connection weight products and outlined 2 identifiable learning patterns of technical skills. CONCLUSION This study outlines the potential utility of ANNs which allows a deeper understanding of the composites of surgical expertise and may contribute to the paradigm shift toward competency-based surgical training.
Collapse
Affiliation(s)
- Aiden Reich
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | | | | | | | | | | | | | | | | |
Collapse
|
8
|
Yilmaz R, Ledwos N, Sawaya R, Winkler-Schwartz A, Mirchi N, Bissonnette V, Fazlollahi AM, Bakhaidar M, Alsayegh A, Sabbagh AJ, Bajunaid K, Del Maestro R. Nondominant Hand Skills Spatial and Psychomotor Analysis During a Complex Virtual Reality Neurosurgical Task-A Case Series Study. Oper Neurosurg (Hagerstown) 2022; 23:22-30. [PMID: 35726926 DOI: 10.1227/ons.0000000000000232] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 02/09/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Virtual reality surgical simulators provide detailed psychomotor performance data, allowing qualitative and quantitative assessment of hand function. The nondominant hand plays an essential role in neurosurgery in exposing the operative area, assisting the dominant hand to optimize task execution, and hemostasis. Outlining expert-level nondominant hand skills may be critical to understand surgical expertise and aid learner training. OBJECTIVE To (1) provide validity for the simulated bimanual subpial tumor resection task and (2) to use this simulation in qualitative and quantitative evaluation of nondominant hand skills for bipolar forceps utilization. METHODS In this case series study, 45 right-handed participants performed a simulated subpial tumor resection using simulated bipolar forceps in the nondominant hand for assisting the surgery and hemostasis. A 10-item questionnaire was used to assess task validity. The nondominant hand skills across 4 expertise levels (neurosurgeons, senior trainees, junior trainees, and medical students) were analyzed by 2 visual models and performance metrics. RESULTS Neurosurgeon median (range) overall satisfaction with the simulated scenario was 4.0/5.0 (2.0-5.0). The visual models demonstrated a decrease in high force application areas on pial surface with increased expertise level. Bipolar-pia mater interactions were more focused around the tumoral region for neurosurgeons and senior trainees. These groups spent more time using the bipolar while interacting with pia. All groups spent significantly higher time in the left upper pial quadrant than other quadrants. CONCLUSION This work introduces new approaches for the evaluation of nondominant hand skills which may help surgical trainees by providing both qualitative and quantitative feedback.
Collapse
Affiliation(s)
- Recai Yilmaz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Nicole Ledwos
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Robin Sawaya
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Alexander Winkler-Schwartz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Nykan Mirchi
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Vincent Bissonnette
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Division of Orthopaedic Surgery, Montreal General Hospital, McGill University, Montreal, Quebec, Canada
| | - Ali M Fazlollahi
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Mohamad Bakhaidar
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmad Alsayegh
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Abdulrahman J Sabbagh
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
- Clinical Skills and Simulation Center, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Khalid Bajunaid
- Department of Surgery, Faculty of Medicine, University of Jeddah, Jeddah, Saudi Arabia
| | - Rolando Del Maestro
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| |
Collapse
|
9
|
Genadry F, Ledwos N, Kumar S, Bowie CR, Daskalakis ZJ, Fischer CE, Herrmann N, Mulsant BH, Pollock BG, Zomorrodi R, Blumberger DM, Rajji TK. PAS-MCI: Design and Rationale for a Randomized Controlled Trial to Enhance Prefrontal Cortical Plasticity and Working Memory in Individuals with Amnestic Mild Cognitive Impairment. The American Journal of Geriatric Psychiatry 2022. [DOI: 10.1016/j.jagp.2022.01.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
10
|
Fazlollahi A, Bakhaidar M, Alsayegh A, Yilmaz R, Winkler-Schwartz A, Langleben I, Mirchi N, Ledwos N, Harley J, Del Maestro R. 510 Artificial Intelligence Tutoring Compared with Expert Instruction in Neurosurgical Simulation Training: A Randomized Controlled Trial. Neurosurgery 2022. [DOI: 10.1227/neu.0000000000001880_510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
|
11
|
Ledwos N, Mirchi N, Yilmaz R, Winkler-Schwartz A, Sawni A, Fazlollahi AM, Bissonnette V, Bajunaid K, Sabbagh AJ, Del Maestro RF. Assessment of learning curves on a simulated neurosurgical task using metrics selected by artificial intelligence. J Neurosurg 2022; 137:1-12. [PMID: 35120309 DOI: 10.3171/2021.12.jns211563] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 12/09/2021] [Indexed: 01/10/2023]
Abstract
OBJECTIVE Understanding the variation of learning curves of experts and trainees for a given surgical procedure is important in implementing formative learning paradigms to accelerate mastery. The study objectives were to use artificial intelligence (AI)-derived metrics to determine the learning curves of participants in 4 groups with different expertise levels who performed a series of identical virtual reality (VR) subpial resection tasks and to identify learning curve differences among the 4 groups. METHODS A total of 50 individuals participated, 14 neurosurgeons, 4 neurosurgical fellows and 10 senior residents (seniors), 10 junior residents (juniors), and 12 medical students. All participants performed 5 repetitions of a subpial tumor resection on the NeuroVR (CAE Healthcare) platform, and 6 a priori-derived metrics selected using the K-nearest neighbors machine learning algorithm were used to assess participant learning curves. Group learning curves were plotted over the 5 trials for each metric. A mixed, repeated-measures ANOVA was performed between the first and fifth trial. For significant interactions (p < 0.05), post hoc Tukey's HSD analysis was conducted to determine the location of the significance. RESULTS Overall, 5 of the 6 metrics assessed had a significant interaction (p < 0.05). The 4 groups, neurosurgeons, seniors, juniors, and medical students, showed an improvement between the first and fifth trial on at least one of the 6 metrics evaluated. CONCLUSIONS Learning curves generated using AI-derived metrics provided novel insights into technical skill acquisition, based on expertise level, during repeated VR-simulated subpial tumor resections, which will allow educators to develop more focused formative educational paradigms for neurosurgical trainees.
Collapse
Affiliation(s)
- Nicole Ledwos
- 1Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University
| | - Nykan Mirchi
- 1Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University
| | - Recai Yilmaz
- 1Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University
| | - Alexander Winkler-Schwartz
- 1Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University
- 3Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Anika Sawni
- 1Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University
| | - Ali M Fazlollahi
- 1Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University
| | - Vincent Bissonnette
- 1Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University
- 2Division of Orthopaedic Surgery, Montreal General Hospital, McGill University
| | - Khalid Bajunaid
- 6Department of Surgery, College of Medicine, University of Jeddah, Jeddah, Saudi Arabia
| | - Abdulrahman J Sabbagh
- 4Division of Neurosurgery, Department of Surgery, College of Medicine, King Abdulaziz University
- 5Clinical Skills and Simulation Center, King Abdulaziz University; and
| | - Rolando F Del Maestro
- 1Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University
- 3Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| |
Collapse
|
12
|
Fazlollahi AM, Bakhaidar M, Alsayegh A, Yilmaz R, Winkler-Schwartz A, Mirchi N, Langleben I, Ledwos N, Sabbagh AJ, Bajunaid K, Harley JM, Del Maestro RF. Effect of Artificial Intelligence Tutoring vs Expert Instruction on Learning Simulated Surgical Skills Among Medical Students: A Randomized Clinical Trial. JAMA Netw Open 2022; 5:e2149008. [PMID: 35191972 PMCID: PMC8864513 DOI: 10.1001/jamanetworkopen.2021.49008] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
IMPORTANCE To better understand the emerging role of artificial intelligence (AI) in surgical training, efficacy of AI tutoring systems, such as the Virtual Operative Assistant (VOA), must be tested and compared with conventional approaches. OBJECTIVE To determine how VOA and remote expert instruction compare in learners' skill acquisition, affective, and cognitive outcomes during surgical simulation training. DESIGN, SETTING, AND PARTICIPANTS This instructor-blinded randomized clinical trial included medical students (undergraduate years 0-2) from 4 institutions in Canada during a single simulation training at McGill Neurosurgical Simulation and Artificial Intelligence Learning Centre, Montreal, Canada. Cross-sectional data were collected from January to April 2021. Analysis was conducted based on intention-to-treat. Data were analyzed from April to June 2021. INTERVENTIONS The interventions included 5 feedback sessions, 5 minutes each, during a single 75-minute training, including 5 practice sessions followed by 1 realistic virtual reality brain tumor resection. The 3 intervention arms included 2 treatment groups, AI audiovisual metric-based feedback (VOA group) and synchronous verbal scripted debriefing and instruction from a remote expert (instructor group), and a control group that received no feedback. MAIN OUTCOMES AND MEASURES The coprimary outcomes were change in procedural performance, quantified as Expertise Score by a validated assessment algorithm (Intelligent Continuous Expertise Monitoring System [ICEMS]; range, -1.00 to 1.00) for each practice resection, and learning and retention, measured from performance in realistic resections by ICEMS and blinded Objective Structured Assessment of Technical Skills (OSATS; range 1-7). Secondary outcomes included strength of emotions before, during, and after the intervention and cognitive load after intervention, measured in self-reports. RESULTS A total of 70 medical students (41 [59%] women and 29 [41%] men; mean [SD] age, 21.8 [2.3] years) from 4 institutions were randomized, including 23 students in the VOA group, 24 students in the instructor group, and 23 students in the control group. All participants were included in the final analysis. ICEMS assessed 350 practice resections, and ICEMS and OSATS evaluated 70 realistic resections. VOA significantly improved practice Expertise Scores by 0.66 (95% CI, 0.55 to 0.77) points compared with the instructor group and by 0.65 (95% CI, 0.54 to 0.77) points compared with the control group (P < .001). Realistic Expertise Scores were significantly higher for the VOA group compared with instructor (mean difference, 0.53 [95% CI, 0.40 to 0.67] points; P < .001) and control (mean difference. 0.49 [95% CI, 0.34 to 0.61] points; P < .001) groups. Mean global OSATS ratings were not statistically significant among the VOA (4.63 [95% CI, 4.06 to 5.20] points), instructor (4.40 [95% CI, 3.88-4.91] points), and control (3.86 [95% CI, 3.44 to 4.27] points) groups. However, on the OSATS subscores, VOA significantly enhanced the mean OSATS overall subscore compared with the control group (mean difference, 1.04 [95% CI, 0.13 to 1.96] points; P = .02), whereas expert instruction significantly improved OSATS subscores for instrument handling vs control (mean difference, 1.18 [95% CI, 0.22 to 2.14]; P = .01). No significant differences in cognitive load, positive activating, and negative emotions were found. CONCLUSIONS AND RELEVANCE In this randomized clinical trial, VOA feedback demonstrated superior performance outcome and skill transfer, with equivalent OSATS ratings and cognitive and emotional responses compared with remote expert instruction, indicating advantages for its use in simulation training. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT04700384.
Collapse
Affiliation(s)
- Ali M. Fazlollahi
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada
| | - Mohamad Bakhaidar
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada
- Division of Neurosurgery, Department of Surgery, College of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmad Alsayegh
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada
- Division of Neurosurgery, Department of Surgery, College of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Recai Yilmaz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada
| | - Alexander Winkler-Schwartz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada
| | - Nykan Mirchi
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Ian Langleben
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada
| | - Nicole Ledwos
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Abdulrahman J. Sabbagh
- Division of Neurosurgery, Department of Surgery, College of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
- Clinical Skills and Simulation Center, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Khalid Bajunaid
- Department of Surgery, College of Medicine, University of Jeddah, Jeddah, Saudi Arabia
| | - Jason M. Harley
- Department of Surgery, McGill University, Montreal, Canada
- Research Institute of the McGill University Health Centre, Montreal, Canada
- Institute for Health Sciences Education, McGill University, Montreal, Canada
- Steinberg Centre for Simulation and Interactive Learning, McGill University, Montreal, Canada
| | - Rolando F. Del Maestro
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada
| |
Collapse
|
13
|
Husain MI, Ledwos N, Fellows E, Baer J, Rosenblat JD, Blumberger DM, Mulsant BH, Castle DJ. Serotonergic psychedelics for depression: What do we know about neurobiological mechanisms of action? Front Psychiatry 2022; 13:1076459. [PMID: 36844032 PMCID: PMC9950579 DOI: 10.3389/fpsyt.2022.1076459] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 12/19/2022] [Indexed: 02/12/2023] Open
Abstract
INTRODUCTION Current treatment options for major depressive disorder (MDD) have limited efficacy and are associated with adverse effects. Recent studies investigating the antidepressant effect of serotonergic psychedelics-also known as classic psychedelics-have promising preliminary results with large effect sizes. In this context, we conducted a review of the putative neurobiological underpinnings of the mechanism of antidepressant action of these drugs. METHODS A narrative review was conducted using PubMed to identify published articles evaluating the antidepressant mechanism of action of serotonergic psychedelics. RESULTS Serotonergic psychedelics have serotonin (5HT)2A agonist or partial agonist effects. Their rapid antidepressant effects may be mediated-in part-by their potent 5HT2A agonism, leading to rapid receptor downregulation. In addition, these psychedelics impact brain derived neurotrophic factor and immunomodulatory responses, both of which may play a role in their antidepressant effect. Several neuroimaging and neurophysiology studies evaluating mechanistic change from a network perspective can help us to further understand their mechanism of action. Some, but not all, data suggest that psychedelics may exert their effects, in part, by disrupting the activity of the default mode network, which is involved in both introspection and self-referential thinking and is over-active in MDD. CONCLUSION The mechanisms of action underlying the antidepressant effect of serotonergic psychedelics remains an active area of research. Several competing theories are being evaluated and more research is needed to determine which ones are supported by the most robust evidence.
Collapse
Affiliation(s)
- Muhammad Ishrat Husain
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Nicole Ledwos
- Centre for Complex Interventions, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Elise Fellows
- Centre for Complex Interventions, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Jenna Baer
- Centre for Complex Interventions, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Joshua D Rosenblat
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
| | - Daniel M Blumberger
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Benoit H Mulsant
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - David J Castle
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Centre for Complex Interventions, Centre for Addiction and Mental Health, Toronto, ON, Canada
| |
Collapse
|
14
|
Alkadri S, Ledwos N, Mirchi N, Reich A, Yilmaz R, Driscoll M, Del Maestro RF. Utilizing a multilayer perceptron artificial neural network to assess a virtual reality surgical procedure. Comput Biol Med 2021; 136:104770. [PMID: 34426170 DOI: 10.1016/j.compbiomed.2021.104770] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/12/2021] [Accepted: 08/13/2021] [Indexed: 11/18/2022]
Abstract
BACKGROUND Virtual reality surgical simulators are a safe and efficient technology for the assessment and training of surgical skills. Simulators allow trainees to improve specific surgical techniques in risk-free environments. Recently, machine learning has been coupled to simulators to classify performance. However, most studies fail to extract meaningful observations behind the classifications and the impact of specific surgical metrics on the performance. One benefit from integrating machine learning algorithms, such as Artificial Neural Networks, to simulators is the ability to extract novel insights into the composites of the surgical performance that differentiate levels of expertise. OBJECTIVE This study aims to demonstrate the benefits of artificial neural network algorithms in assessing and analyzing virtual surgical performances. This study applies the algorithm on a virtual reality simulated annulus incision task during an anterior cervical discectomy and fusion scenario. DESIGN An artificial neural network algorithm was developed and integrated. Participants performed the simulated surgical procedure on the Sim-Ortho simulator. Data extracted from the annulus incision task were extracted to generate 157 surgical performance metrics that spanned three categories (motion, safety, and efficiency). SETTING Musculoskeletal Biomechanics Research Lab; Neurosurgical Simulation and Artificial Intelligence Learning Center, McGill University, Montreal, Canada. PARTICIPANTS Twenty-three participants were recruited and divided into 3 groups: 11 post-residents, 5 senior and 7 junior residents. RESULTS An artificial neural network model was trained on nine selected surgical metrics, spanning all three categories and achieved 80% testing accuracy. CONCLUSIONS This study outlines the benefits of integrating artificial neural networks to virtual reality surgical simulators in understanding composites of expertise performance.
Collapse
Affiliation(s)
- Sami Alkadri
- Musculoskeletal Biomechanics Research Lab, Department of Mechanical Engineering, McGill University, Macdonald Engineering Building, 815 Sherbrooke St W, Montreal, H3A 2K7, QC, Canada
| | - Nicole Ledwos
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, 3801 University Street, Room E2.89, H3A 2B4, Montreal, Quebec, Canada
| | - Nykan Mirchi
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, 3801 University Street, Room E2.89, H3A 2B4, Montreal, Quebec, Canada
| | - Aiden Reich
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, 3801 University Street, Room E2.89, H3A 2B4, Montreal, Quebec, Canada
| | - Recai Yilmaz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, 3801 University Street, Room E2.89, H3A 2B4, Montreal, Quebec, Canada
| | - Mark Driscoll
- Musculoskeletal Biomechanics Research Lab, Department of Mechanical Engineering, McGill University, Macdonald Engineering Building, 815 Sherbrooke St W, Montreal, H3A 2K7, QC, Canada.
| | - Rolando F Del Maestro
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, 3801 University Street, Room E2.89, H3A 2B4, Montreal, Quebec, Canada
| |
Collapse
|
15
|
Ledwos N, Mirchi N, Bissonnette V, Winkler-Schwartz A, Yilmaz R, Del Maestro RF. Virtual Reality Anterior Cervical Discectomy and Fusion Simulation on the Novel Sim-Ortho Platform: Validation Studies. Oper Neurosurg (Hagerstown) 2020; 20:74-82. [DOI: 10.1093/ons/opaa269] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 06/28/2020] [Indexed: 11/13/2022] Open
Abstract
ABSTRACT
BACKGROUND
Virtual reality spine simulators are emerging as potential educational tools to assess and train surgical procedures in safe environments. Analysis of validity is important in determining the educational utility of these systems.
OBJECTIVE
To assess face, content, and construct validity of a C4-C5 anterior cervical discectomy and fusion simulation on the Sim-Ortho virtual reality platform, developed by OSSimTechTM (Montreal, Canada) and the AO Foundation (Davos, Switzerland).
METHODS
Spine surgeons, spine fellows, along with neurosurgical and orthopedic residents, performed a simulated C4-C5 anterior cervical discectomy and fusion on the Sim-Ortho system. Participants were separated into 3 categories: post-residents (spine surgeons and spine fellows), senior residents, and junior residents. A Likert scale was used to assess face and content validity. Construct validity was evaluated by investigating differences between the 3 groups on metrics derived from simulator data. The Kruskal-Wallis test was employed to compare groups and a post-hoc Dunn's test with a Bonferroni correction was utilized to investigate differences between groups on significant metrics.
RESULTS
A total of 21 individuals were included: 9 post-residents, 5 senior residents, and 7 junior residents. The post-resident group rated face and content validity, median ≥4, for the overall procedure and at least 1 tool in each of the 4 steps. Significant differences (P < .05) were found between the post-resident group and senior and/or junior residents on at least 1 metric for each component of the simulation.
CONCLUSION
The C4-C5 anterior cervical discectomy and fusion simulation on the Sim-Ortho platform demonstrated face, content, and construct validity suggesting its utility as a formative educational tool.
Collapse
Affiliation(s)
- Nicole Ledwos
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Nykan Mirchi
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Vincent Bissonnette
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
- Division of Orthopaedic Surgery, Montreal General Hospital, McGill University, Montreal, Canada
| | - Alexander Winkler-Schwartz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Recai Yilmaz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Rolando F Del Maestro
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| |
Collapse
|
16
|
Mirchi N, Bissonnette V, Ledwos N, Winkler-Schwartz A, Yilmaz R, Karlik B, Del Maestro RF. Artificial Neural Networks to Assess Virtual Reality Anterior Cervical Discectomy Performance. Oper Neurosurg (Hagerstown) 2019; 19:65-75. [DOI: 10.1093/ons/opz359] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 09/04/2019] [Indexed: 11/14/2022] Open
Abstract
Abstract
BACKGROUND
Virtual reality surgical simulators provide a safe environment for trainees to practice specific surgical scenarios and allow for self-guided learning. Artificial intelligence technology, including artificial neural networks, offers the potential to manipulate large datasets from simulators to gain insight into the importance of specific performance metrics during simulated operative tasks.
OBJECTIVE
To distinguish performance in a virtual reality-simulated anterior cervical discectomy scenario, uncover novel performance metrics, and gain insight into the relative importance of each metric using artificial neural networks.
METHODS
Twenty-one participants performed a simulated anterior cervical discectomy on the novel virtual reality Sim-Ortho simulator. Participants were divided into 3 groups, including 9 post-resident, 5 senior, and 7 junior participants. This study focused on the discectomy portion of the task. Data were recorded and manipulated to calculate metrics of performance for each participant. Neural networks were trained and tested and the relative importance of each metric was calculated.
RESULTS
A total of 369 metrics spanning 4 categories (safety, efficiency, motion, and cognition) were generated. An artificial neural network was trained on 16 selected metrics and tested, achieving a training accuracy of 100% and a testing accuracy of 83.3%. Network analysis identified safety metrics, including the number of contacts on spinal dura, as highly important.
CONCLUSION
Artificial neural networks classified 3 groups of participants based on expertise allowing insight into the relative importance of specific metrics of performance. This novel methodology aids in the understanding of which components of surgical performance predominantly contribute to expertise.
Collapse
Affiliation(s)
- Nykan Mirchi
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Vincent Bissonnette
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
- Division of Orthopaedic Surgery, Montreal General Hospital, McGill University, Montreal, Canada
| | - Nicole Ledwos
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Alexander Winkler-Schwartz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Recai Yilmaz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Bekir Karlik
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Rolando F Del Maestro
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| |
Collapse
|
17
|
Bissonnette V, Mirchi N, Ledwos N, Alsidieri G, Winkler-Schwartz A, Del Maestro RF. Artificial Intelligence Distinguishes Surgical Training Levels in a Virtual Reality Spinal Task. J Bone Joint Surg Am 2019; 101:e127. [PMID: 31800431 PMCID: PMC7406145 DOI: 10.2106/jbjs.18.01197] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
BACKGROUND With the emergence of competency-based training, the current evaluation scheme of surgical skills is evolving to include newer methods of assessment and training. Artificial intelligence through machine learning algorithms can utilize extensive data sets to analyze operator performance. This study aimed to address 3 questions: (1) Can artificial intelligence uncover novel metrics of surgical performance? (2) Can support vector machine algorithms be trained to differentiate "senior" and "junior" participants who are executing a virtual reality hemilaminectomy? (3) Can other algorithms achieve a good classification performance? METHODS Participants from 4 Canadian universities were divided into 2 groups according to their training level (senior and junior) and were asked to perform a virtual reality hemilaminectomy. The position, angle, and force application of the simulated burr and suction instruments, along with tissue volumes that were removed, were recorded at 20-ms intervals. Raw data were manipulated to create metrics to train machine learning algorithms. Five algorithms, including a support vector machine, were trained to predict whether the task was performed by a senior or junior participant. The accuracy of each algorithm was assessed through leave-one-out cross-validation. RESULTS Forty-one individuals were enrolled (22 senior and 19 junior participants). Twelve metrics related to safety of the procedure, efficiency, motion of the tools, and coordination were selected. Following cross-validation, the support vector machine achieved a 97.6% accuracy. The other algorithms achieved accuracy of 92.7%, 87.8%, 70.7%, and 65.9%, respectively. CONCLUSIONS Artificial intelligence defined novel metrics of surgical performance and outlined training levels in a virtual reality spinal simulation procedure. CLINICAL RELEVANCE The significance of these results lies in the potential of artificial intelligence to complement current educational paradigms and better prepare residents for surgical procedures.
Collapse
Affiliation(s)
- Vincent Bissonnette
- Neurosurgical Simulation & Artificial Intelligence Learning Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Division of Orthopaedic Surgery, Montreal General Hospital, McGill University, Montreal, Quebec, Canada
| | - Nykan Mirchi
- Neurosurgical Simulation & Artificial Intelligence Learning Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Nicole Ledwos
- Neurosurgical Simulation & Artificial Intelligence Learning Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Ghusn Alsidieri
- Neurosurgical Simulation & Artificial Intelligence Learning Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Alexander Winkler-Schwartz
- Neurosurgical Simulation & Artificial Intelligence Learning Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Rolando F Del Maestro
- Neurosurgical Simulation & Artificial Intelligence Learning Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| |
Collapse
|
18
|
Winkler-Schwartz A, Bissonnette V, Mirchi N, Ponnudurai N, Yilmaz R, Ledwos N, Siyar S, Azarnoush H, Karlik B, Del Maestro RF. Artificial Intelligence in Medical Education: Best Practices Using Machine Learning to Assess Surgical Expertise in Virtual Reality Simulation. J Surg Educ 2019; 76:1681-1690. [PMID: 31202633 DOI: 10.1016/j.jsurg.2019.05.015] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 05/17/2019] [Accepted: 05/22/2019] [Indexed: 05/20/2023]
Abstract
OBJECTIVE Virtual reality simulators track all movements and forces of simulated instruments, generating enormous datasets which can be further analyzed with machine learning algorithms. These advancements may increase the understanding, assessment and training of psychomotor performance. Consequently, the application of machine learning techniques to evaluate performance on virtual reality simulators has led to an increase in the volume and complexity of publications which bridge the fields of computer science, medicine, and education. Although all disciplines stand to gain from research in this field, important differences in reporting exist, limiting interdisciplinary communication and knowledge transfer. Thus, our objective was to develop a checklist to provide a general framework when reporting or analyzing studies involving virtual reality surgical simulation and machine learning algorithms. By including a total score as well as clear subsections of the checklist, authors and reviewers can both easily assess the overall quality and specific deficiencies of a manuscript. DESIGN The Machine Learning to Assess Surgical Expertise (MLASE) checklist was developed to help computer science, medicine, and education researchers ensure quality when producing and reviewing virtual reality manuscripts involving machine learning to assess surgical expertise. SETTING This study was carried out at the McGill Neurosurgical Simulation and Artificial Intelligence Learning Centre. PARTICIPANTS The authors applied the checklist to 12 articles using machine learning to assess surgical expertise in virtual reality simulation, obtained through a systematic literature review. RESULTS Important differences in reporting were found between medical and computer science journals. The medical journals proved stronger in discussion quality and weaker in areas related to study design. The opposite trends were observed in computer science journals. CONCLUSIONS This checklist will aid in narrowing the knowledge divide between computer science, medicine, and education: helping facilitate the burgeoning field of machine learning assisted surgical education.
Collapse
Affiliation(s)
- Alexander Winkler-Schwartz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - Vincent Bissonnette
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Division of Orthopedic Surgery, Montreal General Hospital, McGill University, Montreal, Quebec, Canada
| | - Nykan Mirchi
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Nirros Ponnudurai
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Recai Yilmaz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Nicole Ledwos
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Samaneh Siyar
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
| | - Hamed Azarnoush
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
| | - Bekir Karlik
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Rolando F Del Maestro
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| |
Collapse
|
19
|
Winkler-Schwartz A, Yilmaz R, Mirchi N, Bissonnette V, Ledwos N, Siyar S, Azarnoush H, Karlik B, Del Maestro R. Machine Learning Identification of Surgical and Operative Factors Associated With Surgical Expertise in Virtual Reality Simulation. JAMA Netw Open 2019; 2:e198363. [PMID: 31373651 DOI: 10.1001/jamanetworkopen.2019.8363] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
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. These findings suggest that algorithms may be capable of classifying surgical expertise with greater granularity and precision than has been previously demonstrated in surgery.
Collapse
Affiliation(s)
- Alexander Winkler-Schwartz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Recai Yilmaz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Nykan Mirchi
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Vincent Bissonnette
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Department of Orthopedic Surgery, McGill University, Montreal, Quebec, Canada
| | - Nicole Ledwos
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Samaneh Siyar
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
| | - Hamed Azarnoush
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
| | - Bekir Karlik
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Rolando Del Maestro
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| |
Collapse
|
20
|
Bugdadi A, Sawaya R, Bajunaid K, Olwi D, Winkler-Schwartz A, Ledwos N, Marwa I, Alsideiri G, Sabbagh AJ, Alotaibi FE, Al-Zhrani G, Maestro RD. Is Virtual Reality Surgical Performance Influenced by Force Feedback Device Utilized? J Surg Educ 2019; 76:262-273. [PMID: 30072262 DOI: 10.1016/j.jsurg.2018.06.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Revised: 05/19/2018] [Accepted: 06/19/2018] [Indexed: 06/08/2023]
Abstract
OBJECTIVE The study objectives were to assess if surgical performance and subjective assessment of a virtual reality simulator platform was influenced by changing force feedback devices. DESIGN Participants used the NeuroVR (formerly NeuroTouch) simulator to perform 5 practice scenarios and a realistic scenario involving subpial resection of a virtual reality brain tumor with simulated bleeding. The influence of force feedback was assessed by utilizing the Omni and Entact haptic systems. Tier 1, tier 2, and tier 2 advanced metrics were used to compare results. Operator subjective assessment of the haptic systems tested utilized seven Likert criteria (score 1 to 5). SETTING The study is carried out at the McGill Neurosurgical Simulation Research and Training Centre, Montreal Neurological Institute and Hospital, Montreal, Canada. PARTICIPANTS Six expert operators in the utilization of the NeuroVR simulator platform. RESULTS No significant differences in surgical performance were found between the two haptic devices. Participants significantly preferred the Entact system on all 7 Likert criteria of subjective assessment. CONCLUSIONS Our results show no statistical differences in virtual reality surgical performance utilizing the two bimanual haptic devices tested. Subjective assessments demonstrated that participants preferred the Entact system. Our results suggest that to maximize realism of the training experience educators employing virtual reality simulators may find it useful to assess expert opinion before choosing a force feedback device.
Collapse
Affiliation(s)
- Abdulgadir Bugdadi
- Neurosurgical Simulation Research and Training Centre, Department of Neurosurgery and Neurology, McGill University, Montreal, Quebec, Canada; Department of Surgery, Faculty of Medicine, Umm Al-Qura University, Makkah Almukarramah, Saudi Arabia.
| | - Robin Sawaya
- Neurosurgical Simulation Research and Training Centre, Department of Neurosurgery and Neurology, McGill University, Montreal, Quebec, Canada
| | - Khalid Bajunaid
- Neurosurgical Simulation Research and Training Centre, Department of Neurosurgery and Neurology, McGill University, Montreal, Quebec, Canada; Division of Neurosurgery, Faculty of Medicine, University of Jeddah, Jeddah, Saudi Arabia
| | - Duaa Olwi
- Neurosurgical Simulation Research and Training Centre, Department of Neurosurgery and Neurology, McGill University, Montreal, Quebec, Canada; King Abdullah International Medical Research Center, Ministry of National Guard Health Affairs, Jeddah, Saudi Arabia
| | - Alexander Winkler-Schwartz
- Neurosurgical Simulation Research and Training Centre, Department of Neurosurgery and Neurology, McGill University, Montreal, Quebec, Canada
| | - Nicole Ledwos
- Neurosurgical Simulation Research and Training Centre, Department of Neurosurgery and Neurology, McGill University, Montreal, Quebec, Canada
| | - Ibrahim Marwa
- Neurosurgical Simulation Research and Training Centre, Department of Neurosurgery and Neurology, McGill University, Montreal, Quebec, Canada
| | - Ghusn Alsideiri
- Neurosurgical Simulation Research and Training Centre, Department of Neurosurgery and Neurology, McGill University, Montreal, Quebec, Canada; Department of Surgery, College of Medicine, Sultan Qaboos University, Muscat, Oman
| | - Abdulrahman Jafar Sabbagh
- Neurosurgical Simulation Research and Training Centre, Department of Neurosurgery and Neurology, McGill University, Montreal, Quebec, Canada; Division of Neurosurgery, Department of Surgery, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia; Clinical Skill and Simulation Center, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Fahad E Alotaibi
- Neurosurgical Simulation Research and Training Centre, Department of Neurosurgery and Neurology, McGill University, Montreal, Quebec, Canada; National Neuroscience Institute, Department of Neurosurgery, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Gmaan Al-Zhrani
- Neurosurgical Simulation Research and Training Centre, Department of Neurosurgery and Neurology, McGill University, Montreal, Quebec, Canada; National Neuroscience Institute, Department of Neurosurgery, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Rolando Del Maestro
- Neurosurgical Simulation Research and Training Centre, Department of Neurosurgery and Neurology, McGill University, Montreal, Quebec, Canada
| |
Collapse
|