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Alkadri S, Del Maestro RF, Driscoll M. Face, content, and construct validity of a novel VR/AR surgical simulator of a minimally invasive spine operation. Med Biol Eng Comput 2024; 62:1887-1897. [PMID: 38403863 DOI: 10.1007/s11517-024-03053-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 02/02/2024] [Indexed: 02/27/2024]
Abstract
Mixed-reality surgical simulators are seen more objective than conventional training. The simulators' utility in training must be established through validation studies. Establish face-, content-, and construct-validity of a novel mixed-reality surgical simulator developed by McGill University, CAE-Healthcare, and DePuy Synthes. This study, approved by a Research Ethics Board, examined a simulated L4-L5 oblique lateral lumbar interbody fusion (OLLIF) scenario. A 5-point Likert scale questionnaire was used. Chi-square test verified validity consensus. Construct validity investigated 276 surgical performance metrics across three groups, using ANOVA, Welch-ANOVA, or Kruskal-Wallis tests. A post-hoc Dunn's test with a Bonferroni correction was used for further analysis on significant metrics. Musculoskeletal Biomechanics Research Lab, McGill University, Montreal, Canada. DePuy Synthes, Johnson & Johnson Family of Companies, research lab. Thirty-four participants were recruited: spine surgeons, fellows, neurosurgical, and orthopedic residents. Only seven surgeons out of the 34 were recruited in a side-by-side cadaver trial, where participants completed an OLLIF surgery first on a cadaver and then immediately on the simulator. Participants were separated a priori into three groups: post-, senior-, and junior-residents. Post-residents rated validity, median > 3, for 13/20 face-validity and 9/25 content-validity statements. Seven face-validity and 12 content-validity statements were rated neutral. Chi-square test indicated agreeability between group responses. Construct validity found eight metrics with significant differences (p < 0.05) between the three groups. Validity was established. Most face-validity statements were positively rated, with few neutrally rated pertaining to the simulation's graphics. Although fewer content-validity statements were validated, most were rated neutral (only four were negatively rated). The findings underscored the importance of using realistic physics-based forces in surgical simulations. Construct validity demonstrated the simulator's capacity to differentiate surgical expertise.
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Affiliation(s)
- Sami Alkadri
- Musculoskeletal Biomechanics Research Lab, Department of Mechanical Engineering, McGill University, Macdonald Engineering Building, 815 Sherbrooke St W, Montreal, QC, H3A 2K7, Canada
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, 2200 Leo Pariseau, Suite 2210, Montreal, QC, H2X 4B3, Canada
| | - Rolando F Del Maestro
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, 2200 Leo Pariseau, Suite 2210, Montreal, QC, H2X 4B3, Canada
| | - Mark Driscoll
- Musculoskeletal Biomechanics Research Lab, Department of Mechanical Engineering, McGill University, Macdonald Engineering Building, 815 Sherbrooke St W, Montreal, QC, H3A 2K7, Canada.
- Orthopaedic Research Lab, Montreal General Hospital, 1650 Cedar Ave (LS1.409), Montreal, QC, H3G 1A4, Canada.
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Jung Y, Muddaluru V, Gandhi P, Pahuta M, Guha D. The Development And Applications Of Augmented And Virtual Reality Technology In Spine Surgery Training: A Systematic Review. Can J Neurol Sci 2024; 51:255-264. [PMID: 37113079 DOI: 10.1017/cjn.2023.46] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
BACKGROUND The COVID-19 pandemic has accelerated the growing global interest in the role of augmented and virtual reality in surgical training. While this technology grows at a rapid rate, its efficacy remains unclear. To that end, we offer a systematic review of the literature summarizing the role of virtual and augmented reality on spine surgery training. METHODS A systematic review of the literature was conducted on May 13th, 2022. PubMed, Web of Science, Medline, and Embase were reviewed for relevant studies. Studies from both orthopedic and neurosurgical spine programs were considered. There were no restrictions placed on the type of study, virtual/augmented reality modality, nor type of procedure. Qualitative data analysis was performed, and all studies were assigned a Medical Education Research Study Quality Instrument (MERSQI) score. RESULTS The initial review identified 6752 studies, of which 16 were deemed relevant and included in the final review, examining a total of nine unique augmented/virtual reality systems. These studies had a moderate methodological quality with a MERSQI score of 12.1 + 1.8; most studies were conducted at single-center institutions, and unclear response rates. Statistical pooling of the data was limited by the heterogeneity of the study designs. CONCLUSION This review examined the applications of augmented and virtual reality systems for training residents in various spine procedures. As this technology continues to advance, higher-quality, multi-center, and long-term studies are required to further the adaptation of VR/AR technologies in spine surgery training programs.
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Affiliation(s)
- Youngkyung Jung
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | | | - Pranjan Gandhi
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Markian Pahuta
- Division of Orthopedic Surgery, Hamilton General Hospital, McMaster University, Hamilton, ON, Canada
| | - Daipayan Guha
- Division of Neurosurgery, Hamilton General Hospital, McMaster University, Hamilton, ON, Canada
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Yilmaz R, Fazlollahi AM, Winkler-Schwartz A, Wang A, Makhani HH, Alsayegh A, Bakhaidar M, Tran DH, Santaguida C, Del Maestro RF. Effect of Feedback Modality on Simulated Surgical Skills Learning Using Automated Educational Systems- A Four-Arm Randomized Control Trial. JOURNAL OF SURGICAL EDUCATION 2024; 81:275-287. [PMID: 38160107 DOI: 10.1016/j.jsurg.2023.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 09/05/2023] [Accepted: 11/01/2023] [Indexed: 01/03/2024]
Abstract
OBJECTIVE To explore optimal feedback methodologies to enhance trainee skill acquisition in simulated surgical bimanual skills learning during brain tumor resections. HYPOTHESES (1) Providing feedback results in better learning outcomes in teaching surgical technical skill when compared to practice alone with no tailored performance feedback. (2) Providing more visual and visuospatial feedback results in better learning outcomes when compared to providing numerical feedback. DESIGN A prospective 4-parallel-arm randomized controlled trial. SETTING Neurosurgical Simulation and Artificial Intelligence Learning Centre, McGill University, Canada. PARTICIPANTS Medical students (n = 120) from 4 Quebec medical schools. RESULTS Participants completed a virtually simulated tumor resection task 5 times while receiving 1 of 4 feedback based on their group allocation: (1) practice-alone without feedback, (2) numerical feedback, (3) visual feedback, and (4) visuospatial feedback. Outcome measures were participants' scores on 14-performance metrics and the number of expert benchmarks achieved during each task. There were no significant differences in the first task which determined baseline performance. A statistically significant interaction between feedback allocation and task repetition was found on the number of benchmarks achieved, F (10.558, 408.257)=3.220, p < 0.001. Participants in all feedback groups significantly improved their performance compared to baseline. The visual feedback group achieved significantly higher number of benchmarks than the practice-alone group by the third repetition of the task, p = 0.005, 95%CI [0.42 3.25]. Visual feedback and visuospatial feedback improved performance significantly by the second repetition of the task, p = 0.016, 95%CI [0.19 2.71] and p = 0.003, 95%CI [0.4 2.57], respectively. CONCLUSION Simulations with autonomous visual computer assistance may be effective pedagogical tools in teaching bimanual operative skills via visual and visuospatial feedback information delivery.
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Affiliation(s)
- Recai Yilmaz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada.
| | - Ali M Fazlollahi
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada
| | - Alexander Winkler-Schwartz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Anna Wang
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Hafila Hassan Makhani
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Ahmad Alsayegh
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & 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
| | - Mohamad Bakhaidar
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & 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
| | - Dan Huy Tran
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Carlo Santaguida
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Rolando F Del Maestro
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada; Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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Almansouri A, Abou Hamdan N, Yilmaz R, Tee T, Pachchigar P, Eskandari M, Agu C, Giglio B, Balasubramaniam N, Bierbrier J, Collins DL, Gueziri HE, Del Maestro RF. Continuous Instrument Tracking in a Cerebral Corticectomy Ex Vivo Calf Brain Simulation Model: Face and Content Validation. Oper Neurosurg (Hagerstown) 2024:01787389-990000000-01017. [PMID: 38190098 DOI: 10.1227/ons.0000000000001044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/13/2023] [Indexed: 01/09/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Subpial corticectomy involving complete lesion resection while preserving pial membranes and avoiding injury to adjacent normal tissues is an essential bimanual task necessary for neurosurgical trainees to master. We sought to develop an ex vivo calf brain corticectomy simulation model with continuous assessment of surgical instrument movement during the simulation. A case series study of skilled participants was performed to assess face and content validity to gain insights into the utility of this training platform, along with determining if skilled and less skilled participants had statistical differences in validity assessment. METHODS An ex vivo calf brain simulation model was developed in which trainees performed a subpial corticectomy of three defined areas. A case series study assessed face and content validity of the model using 7-point Likert scale questionnaires. RESULTS Twelve skilled and 11 less skilled participants were included in this investigation. Overall median scores of 6.0 (range 4.0-6.0) for face validity and 6.0 (range 3.5-7.0) for content validity were determined on the 7-point Likert scale, with no statistical differences between skilled and less skilled groups identified. CONCLUSION A novel ex vivo calf brain simulator was developed to replicate the subpial resection procedure and demonstrated face and content validity.
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Affiliation(s)
- Abdulrahman Almansouri
- 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
| | - Nour Abou Hamdan
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Recai Yilmaz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Trisha Tee
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Puja Pachchigar
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | | | - Chinyelum Agu
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Bianca Giglio
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Neevya Balasubramaniam
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Joshua Bierbrier
- Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - D Louis Collins
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Houssem-Eddine Gueziri
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Rolando F 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
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Lai C, Lui JT, de Lotbiniere-Bassett M, Chen JM, Lin VY, Agrawal SK, Blevins NH, Ladak HM, Pirouzmand F. Virtual Reality Simulation for the Middle Cranial Fossa Approach: A Validation Study. Oper Neurosurg (Hagerstown) 2024; 26:78-85. [PMID: 37747333 DOI: 10.1227/ons.0000000000000915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 07/22/2023] [Indexed: 09/26/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Virtual reality (VR) surgical rehearsal is an educational tool that exists in a safe environment. Validation is necessary to establish the educational value of this platform. The middle cranial fossa (MCF) is ideal for simulation because trainees have limited exposure to this approach and it has considerable complication risk. Our objectives were to assess the face, content, and construct validities of an MCF VR simulation, as well as the change in performance across serial simulations. METHODS Using high-resolution volumetric data sets of human cadavers, the authors generated a high-fidelity visual and haptic rendering of the MCF approach using CardinalSim software. Trainees from Neurosurgery and Otolaryngology-Head and Neck Surgery at two Canadian academic centers performed MCF dissections on this VR platform. Randomization was used to assess the effect of enhanced VR interaction. Likert scales were used to assess the face and content validities. Performance metrics and pre- and postsimulation test scores were evaluated. Construct validity was evaluated by examining the effect of the training level on simulation performance. RESULTS Twenty trainees were enrolled. Face and content validities were achieved in all domains. Construct validity, however, was not demonstrated. Postsimulation test scores were significantly higher than presimulation test scores ( P < .001 ). Trainees demonstrated statistically significant improvement in the time to complete dissections ( P < .001 ), internal auditory canal skeletonization ( P < .001 ), completeness of the anterior petrosectomy ( P < .001 ), and reduced number of injuries to critical structures ( P = .001 ). CONCLUSION This MCF VR simulation created using CardinalSim demonstrated face and content validities. Construct validity was not established because no trainee included in the study had previous MCF approach experience, which further emphasizes the importance of simulation. When used as a formative educational adjunct in both Neurosurgery and Otolaryngology-Head and Neck Surgery, this simulation has the potential to enhance understanding of the complex anatomic relationships of critical neurovascular structures.
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Affiliation(s)
- Carolyn Lai
- Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto , Ontario , Canada
| | - Justin T Lui
- Section of Otolaryngology-Head & Neck Surgery, Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary , Alberta, Canada
| | - Madeleine de Lotbiniere-Bassett
- Section of Neurosurgery, Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary , Alberta, Canada
| | - Joseph M Chen
- Department of Otolaryngology-Head & Neck Surgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto , Ontario , Canada
| | - Vincent Y Lin
- Department of Otolaryngology-Head & Neck Surgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto , Ontario , Canada
| | - Sumit K Agrawal
- Department of Otolaryngology-Head & Neck Surgery, London Health Sciences Centre-University Hospital, Western University, London , Ontario , Canada
| | - Nikolas H Blevins
- Department of Otolaryngology-Head & Neck Surgery, Stanford University, Palo Alto , California , USA
| | - Hanif M Ladak
- Department of Medical Biophysics, Western University, London , Ontario , Canada
- Department of Electrical & Computer Engineering, Western University, London , Ontario , Canada
| | - Farhad Pirouzmand
- Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto , Ontario , Canada
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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] [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.
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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
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Cate G, Barnes CL, Dickinson KJ. Simulation training to retool practicing orthopedic surgeons is rare. GLOBAL SURGICAL EDUCATION : JOURNAL OF THE ASSOCIATION FOR SURGICAL EDUCATION 2023; 2:57. [PMID: 38013868 PMCID: PMC10203688 DOI: 10.1007/s44186-023-00136-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 04/05/2023] [Accepted: 05/14/2023] [Indexed: 11/29/2023]
Abstract
Purpose Modern surgical practice is continuously changing as technology develops. New techniques are often implemented after a surgeon has made the transition to independent clinical practice. There is therefore a need to 'retool' technical skills. Additionally, practicing surgeons must maintain and develop skills such as leadership, communication, critical thinking, teaching, and mentoring. Our aim was to perform a scoping review to assess the current status of simulation education for practicing Orthopedic Surgeons (OS). Methods A 10 year search of PubMed, ERIC, and Web of Science was performed with a medical librarian. Controlled vocabulary Medical Subject Headings terms and natural language were developed with subject matter experts describing simulation, training and OS. Two trained reviewers evaluated all abstracts for inclusion. Exclusion criteria were articles that did not assess simulation education involving practicing OS. Data were extracted from the included full text articles by two reviewers: details of study design, type of participants, type of simulation and role of OS in the educational event. Results Initial search identified 1824 articles of which 443 were duplicates, and 1381 articles were further screened. Of these, 1155 were excluded, 226 full text articles were assessed for eligibility and 80 included in analysis. Most were published in the last 6 years and from the United States. The majority (99%) described technical skill simulations (arthroscopy 56%, screw placement 23%, ligament reconstruction 19%). OS were rarely the only learners with 91% studies also having residents participate. OS were the targeted learner in 6% studies. OS provided content validity for 15 (19%) and construct validity in 59 (74%) studies. Conclusions Simulation training to educate practicing OS is rare. OS are often used to validate work rather than being the center of an educational endeavor. A refocusing is needed to provide adequate training for practicing surgeons to retool skills as new techniques become available.
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Affiliation(s)
- Graham Cate
- College of Medicine, University of Arkansas for Medical Sciences, Little Rock, USA
| | - C. Lowry Barnes
- Department of Orthopedic Surgery, University of Arkansas for Medical Sciences, Little Rock, USA
| | - Karen J. Dickinson
- College of Medicine, University of Arkansas for Medical Sciences, Little Rock, USA
- Office of Interprofessional Education, University of Arkansas for Medical Sciences, Little Rock, USA
- Department of Surgery, University of Arkansas for Medical Sciences, Little Rock, AR USA
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Cate G, Barnes J, Cherney S, Stambough J, Bumpass D, Barnes CL, Dickinson KJ. Current status of virtual reality simulation education for orthopedic residents: the need for a change in focus. GLOBAL SURGICAL EDUCATION : JOURNAL OF THE ASSOCIATION FOR SURGICAL EDUCATION 2023; 2:46. [PMID: 38013875 PMCID: PMC10032253 DOI: 10.1007/s44186-023-00120-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 02/23/2023] [Accepted: 03/06/2023] [Indexed: 03/24/2023]
Abstract
Introduction Advances in technology are changing surgical education. Simulation provides an important adjunct to operative experience. This pedagogy has arguably become more important in light of the COVID-19 pandemic, with resultant reduction in operative exposure for trainees. Virtual reality (VR) simulators may provide significant contribution to experiential learning; however, much of the investigative focus to date has, correctly, been on establishing validity evidence for these constructs. The aim of this work was to perform a scoping review to assess the current status of VR simulation education to determine curricular development efforts for orthopedic residents. Methods With a trained medical librarian, searches of PubMed, EMBASE, and Web of Science were conducted for all articles in the last 10 years (September 2011-September 2021). Controlled vocabulary Medical Subject Headings (MeSH) terms and natural language developed with subject matter experts describing virtual reality or VR simulation and orthopedic training were used. Two trained reviewers evaluated all abstracts for inclusion. Exclusion criteria were all articles that did not assess VR simulation education involving orthopedic residents. Data were extracted from the included full-text articles including: study design, type of participants, type of VR simulation, simulated orthopedic skill, type of educational event, learner assessment including Kirkpatrick's level, assessment of quality using the Medical Education Research Study Quality Instrument (MERSQI), and level of effectiveness (LoE). Results Initial search identified 1,394 articles, of which 61 were included in the final qualitative synthesis. The majority (54%) were published in 2019- 2021, 49% in Europe. The commonest VR simulator was ArthroS (23%) and the commonest simulated skill was knee arthroscopy (33%). The majority of studies (70%) focused on simulator validation. Twenty-three studies described an educational module or curriculum, and of the 21 (34%) educational modules, 43% were one-off events. Most modules (18/21, 86%) assessed learners at Kirkpatrick level 2. With regard to methodological quality, 44% of studies had MERSQI 11.5-15 and 89% of studies had LoE of 2. Two studies had LoE of 3. Conclusion Current literature pertaining to VR training for orthopedic residents is focused on establishing validity and rarely forms part of a curriculum. Where the focus is education, the majority are discrete educational modules and do not teach a comprehensive amalgam of orthopedic skills. This suggests focus is needed to embed VR simulation training within formal curricula efforts guided by the work of Kern, and assess the efficacy of these against patient outcomes.
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Affiliation(s)
- Graham Cate
- College of Medicine, University of Arkansas for Medical Sciences, Little Rock, USA
| | - Jack Barnes
- Department of Orthopedics, University of Arkansas for Medical Sciences, Little Rock, USA
| | - Steven Cherney
- Department of Orthopedics, University of Arkansas for Medical Sciences, Little Rock, USA
| | - Jeffrey Stambough
- Department of Orthopedics, University of Arkansas for Medical Sciences, Little Rock, USA
| | - David Bumpass
- Department of Orthopedics, University of Arkansas for Medical Sciences, Little Rock, USA
| | - C. Lowry Barnes
- Department of Orthopedics, University of Arkansas for Medical Sciences, Little Rock, USA
| | - Karen J. Dickinson
- Department of Surgery, University of Arkansas for Medical Sciences, Little Rock, AR USA
- Office of Interprofessional Education, University of Arkansas for Medical Sciences, Little Rock, USA
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Natheir S, Christie S, Yilmaz R, Winkler-Schwartz A, Bajunaid K, Sabbagh AJ, Werthner P, Fares J, Azarnoush H, Del Maestro R. Utilizing artificial intelligence and electroencephalography to assess expertise on a simulated neurosurgical task. Comput Biol Med 2023; 152:106286. [PMID: 36502696 DOI: 10.1016/j.compbiomed.2022.106286] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 10/18/2022] [Accepted: 10/25/2022] [Indexed: 11/17/2022]
Abstract
Virtual reality surgical simulators have facilitated surgical education by providing a safe training environment. Electroencephalography (EEG) has been employed to assess neuroelectric activity during surgical performance. Machine learning (ML) has been applied to analyze EEG data split into frequency bands. Although EEG is widely used in fields requiring expert performance, it has yet been used to classify surgical expertise. Thus, the goals of this study were to (a) develop an ML model to accurately differentiate skilled and less-skilled performance using EEG data recorded during a simulated surgery, (b) explore the relative importance of each EEG bandwidth to expertise, and (c) analyze differences in EEG band powers between skilled and less-skilled individuals. We hypothesized that EEG recordings during a virtual reality surgery task would accurately predict the expertise level of the participant. Twenty-one participants performed three simulated brain tumor resection procedures on the NeuroVR™ platform (CAE Healthcare, Montreal, Canada) while EEG data was recorded. Participants were divided into 2 groups. The skilled group was composed of five neurosurgeons and five senior neurosurgical residents (PGY4-6), and the less-skilled group was composed of six junior residents (PGY1-3) and five medical students. A total of 13 metrics from EEG frequency bands and ratios (e.g., alpha, theta/beta ratio) were generated. Seven ML model types were trained using EEG activity to differentiate between skilled and less-skilled groups. The artificial neural network achieved the highest testing accuracy of 100% (AUROC = 1.0). Model interpretation via Shapley analysis identified low alpha (8-10 Hz) as the most important metric for classifying expertise. Skilled surgeons displayed higher (p = 0.044) low-alpha than the less-skilled group. Furthermore, skilled surgeons displayed significantly lower TBR (p = 0.048) and significantly higher beta (13-30 Hz, p = 0.049), beta 1 (15-18 Hz, p = 0.014), and beta 2 (19-22 Hz, p = 0.015), thus establishing these metrics as important markers of expertise. ACGME CORE COMPETENCIES: Practice-Based Learning and Improvement.
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Affiliation(s)
- Sharif Natheir
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - Sommer Christie
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Recai Yilmaz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Alexander Winkler-Schwartz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Khalid Bajunaid
- Department of Surgery, College of Medicine, University of Jeddah, Jeddah, Saudi Arabia
| | - 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
| | - Penny Werthner
- University of Calgary, Faculty of Kinesiology, Calgary, Alberta, Canada
| | - Jawad Fares
- Department of Neurological Surgery Feinberg School of Medicine, Northwestern University Chicago, Illinois, USA
| | - Hamed Azarnoush
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
| | - Rolando Del Maestro
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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10
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Stott B, Driscoll M. Face and content validity of analog surgical instruments on a novel physics-driven minimally invasive spinal fusion surgical simulator. Med Biol Eng Comput 2022; 60:2771-2778. [DOI: 10.1007/s11517-022-02635-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 07/06/2022] [Indexed: 10/17/2022]
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11
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Cotter T, Mongrain R, Driscoll M. Vacuum curette lumbar discectomy mechanics for use in spine surgical training simulators. Sci Rep 2022; 12:13517. [PMID: 35933556 PMCID: PMC9357010 DOI: 10.1038/s41598-022-17512-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/26/2022] [Indexed: 11/19/2022] Open
Abstract
Simulation in surgical training is a growing field and this study aims to understand the force and torque experienced during lumbar spine surgery to design simulator haptic feedback. It was hypothesized that force and torque would differ among lumbar spine levels and the amount of tissue removed by ≥ 7%, which would be detectable to a user. Force and torque profiles were measured during vacuum curette insertion and torsion, respectively, in multiple spinal levels on two cadavers. Multiple tests per level were performed. Linear and torsional resistances of 2.1 ± 1.6 N/mm and 5.6 ± 4.3 N mm/°, respectively, were quantified. Statistically significant differences were found in linear and torsional resistances between all passes through disc tissue (both p = 0.001). Tool depth (p < 0.001) and lumbar level (p < 0.001) impacted torsional resistance while tool speed affected linear resistance (p = 0.022). Average differences in these statistically significant comparisons were ≥ 7% and therefore detectable to a surgeon. The aforementioned factors should be considered when developing haptic force and torque feedback, as they will add to the simulated lumbar discectomy realism. These data can additionally be used inform next generation tool design. Advances in training and tools may help improve future surgeon training.
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Affiliation(s)
- Trevor Cotter
- Musculoskeletal Biomechanics Research Lab, Department of Mechanical Engineering, McGill University, Montreal, H3A 0C4, Canada.,Orthopedic Research Laboratory, Montreal General Hospital, Montreal, QC, H3H 1V8, Canada
| | - Rosaire Mongrain
- Musculoskeletal Biomechanics Research Lab, Department of Mechanical Engineering, McGill University, Montreal, H3A 0C4, Canada
| | - Mark Driscoll
- Musculoskeletal Biomechanics Research Lab, Department of Mechanical Engineering, McGill University, Montreal, H3A 0C4, Canada. .,Orthopedic Research Laboratory, Montreal General Hospital, Montreal, QC, H3H 1V8, Canada.
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12
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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] [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.
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Affiliation(s)
- Aiden Reich
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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13
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Cotter TR, Mongrain R, Driscoll M. Design Synthesis of a Robotic Uniaxial Torque Device for Orthopedic Haptic Simulation. J Med Device 2022. [DOI: 10.1115/1.4054344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Abstract
Robotic devices are commonly used in surgical simulators to provide tactile, or haptic, feedback. They can provide customized feedback that can be rapidly modified with minimal hardware changes in comparison to non-robotic systems. This work describes the design, development, and evaluation of one such tool: a novel uniaxial torque haptic device for a surgical training simulator. The objective of the work was to design a single connection haptic device that could augment an existing six degree of freedom haptic device to mimic a Concorde Clear vacuum curette. Design and evaluations focused on the tool's ability to deliver adequate torque, imitate a surgical tool, and be integrated into the haptic device. Twenty-nine surgeons tested the tool in the simulator and evaluated it via a questionnaire. The device was found to deliver the 800 N·mm of torque necessary to mimic an orthopedic procedure. Surgeons found it accurately imitated surgical tool physical appearance and maneuverability, scoring them 3.9±1.0 and 3.3±1.2, respectively, on a 1-5 Likert scale. By virtue of the functionality necessary for testing and evaluation, the device could be connected to the haptic device for mechanical and electrical engagement. This device is a step forward in the field of augmentable haptic devices for surgical simulation. By changing the number of robotically-controlled degrees of freedom of a haptic device, existing devices can be tuned to meet the demands of a particular simulator, which has the potential to improve surgeon training standards.
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Affiliation(s)
- Trevor R Cotter
- Department of Mechanical Engineering, McGill University, Montréal, Quebec H3A 0G4, Canada; Orthopaedic Research Laboratory, Montréal General Hospital, Montréal, Quebec H3G 1A4, Canada
| | - Rosaire Mongrain
- Department of Mechanical Engineering, McGill University, Montréal, Quebec H3A 0G4, Canada
| | - Mark Driscoll
- Department of Mechanical Engineering, McGill University, Montréal, Quebec H3A 0G4, Canada; Orthopaedic Research Laboratory, Montréal General Hospital, Montréal, Quebec H3G 1A4, Canada
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14
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Scott H, Griffin C, Coggins W, Elberson B, Abdeldayem M, Virmani T, Larson-Prior LJ, Petersen E. Virtual Reality in the Neurosciences: Current Practice and Future Directions. Front Surg 2022; 8:807195. [PMID: 35252318 PMCID: PMC8894248 DOI: 10.3389/fsurg.2021.807195] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 12/30/2021] [Indexed: 01/05/2023] Open
Abstract
Virtual reality has made numerous advancements in recent years and is used with increasing frequency for education, diversion, and distraction. Beginning several years ago as a device that produced an image with only a few pixels, virtual reality is now able to generate detailed, three-dimensional, and interactive images. Furthermore, these images can be used to provide quantitative data when acting as a simulator or a rehabilitation device. In this article, we aim to draw attention to these areas, as well as highlight the current settings in which virtual reality (VR) is being actively studied and implemented within the field of neurosurgery and the neurosciences. Additionally, we discuss the current limitations of the applications of virtual reality within various settings. This article includes areas in which virtual reality has been used in applications both inside and outside of the operating room, such as pain control, patient education and counseling, and rehabilitation. Virtual reality's utility in neurosurgery and the neurosciences is widely growing, and its use is quickly becoming an integral part of patient care, surgical training, operative planning, navigation, and rehabilitation.
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Affiliation(s)
- Hayden Scott
- College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- *Correspondence: Hayden Scott
| | - Connor Griffin
- College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - William Coggins
- Department of Neurosurgery, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Brooke Elberson
- Department of Neurosurgery, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Mohamed Abdeldayem
- Department of Anesthesiology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Tuhin Virmani
- Department of Neurology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Linda J. Larson-Prior
- Department of Neurology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Department of Psychiatry, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Department of Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Erika Petersen
- Department of Anesthesiology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
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15
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Patel S, Alkadri S, Driscoll M. Development and Validation of a Mixed Reality Configuration of a Simulator for a Minimally Invasive Spine Surgery Using the Workspace of a Haptic Device and Simulator Users. BIOMED RESEARCH INTERNATIONAL 2021; 2021:2435126. [PMID: 35005014 PMCID: PMC8741356 DOI: 10.1155/2021/2435126] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 12/07/2021] [Indexed: 11/18/2022]
Abstract
Most surgical simulators leverage virtual or bench models to simulate reality. This study proposes and validates a method for workspace configuration of a surgical simulator which utilizes a haptic device for interaction with a virtual model and a bench model to provide additional tactile feedback based on planned surgical manoeuvers. Numerical analyses were completed to determine the workspace and position of a haptic device, relative to the bench model, used in the surgical simulator, and the determined configuration was validated using device limitations and user data from surgical and nonsurgical users. For the validation, surgeons performed an identical surgery on a cadaver prior to using the simulator, and their trajectories were then compared to the determined workspace for the haptic device. The configuration of the simulator was determined appropriate through workspace analysis and the collected user trajectories. Statistical analyses suggest differences in trajectories between the participating surgeons which were not affected by the imposed haptic workspace. This study, therefore, demonstrates a method to optimally position a haptic device with respect to a bench model while meeting the manoeuverability needs of a surgical procedure. The validation method identified workspace position and user trajectory towards ideal configuration of a mixed reality simulator.
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Affiliation(s)
- Sneha Patel
- Department of Mechanical Engineering, McGill University, MacDonald Engineering Building, 817 Rue Sherbrooke Ouest #270, Montréal, Québec, Canada H3A 0C3
| | - Sami Alkadri
- Department of Mechanical Engineering, McGill University, MacDonald Engineering Building, 817 Rue Sherbrooke Ouest #270, Montréal, Québec, Canada H3A 0C3
| | - Mark Driscoll
- Department of Mechanical Engineering, McGill University, MacDonald Engineering Building, 817 Rue Sherbrooke Ouest #270, Montréal, Québec, Canada H3A 0C3
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16
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Alsayegh A, Bakhaidar M, Winkler-Schwartz A, Yilmaz R, Del Maestro RF. Best Practices Using Ex Vivo Animal Brain Models in Neurosurgical Education to Assess Surgical Expertise. World Neurosurg 2021; 155:e369-e381. [PMID: 34419656 DOI: 10.1016/j.wneu.2021.08.061] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 08/12/2021] [Accepted: 08/12/2021] [Indexed: 11/15/2022]
Abstract
BACKGROUND Ex vivo animal brain simulation models are being increasingly used for neurosurgical training because these models can replicate human brain conditions. The goal of the present report is to provide the neurosurgical community interested in using ex vivo animal brain simulation models with guidelines for comprehensively and rigorously conducting, documenting, and assessing this type of research. METHODS In consultation with an interdisciplinary group of physicians and researchers involved in ex vivo models and a review of the literature on the best practices guidelines for simulation research, we developed the "ex vivo brain model to assess surgical expertise" (EVBMASE) checklist. The EVBMASE checklist provides a comprehensive quantitative framework for analyzing and reporting studies involving these models. We applied The EVBMASE checklist to the studies reported of ex vivo animal brain models to document how current ex vivo brain simulation models are used to train surgical expertise. RESULTS The EVBMASE checklist includes defined subsections and a total score of 20, which can help investigators improve studies and provide readers with techniques to better assess the quality and any deficiencies of the research. We classified 18 published ex vivo brain models into modified (group 1) and nonmodified (group 2) models. The mean total EVBMASE score was 11 (55%) for group 1 and 4.8 (24.2%) for group 2, a statistically significant difference (P = 0.006) mainly attributed to differences in the simulation study design section (P = 0.003). CONCLUSIONS The present findings should help contribute to more rigorous application, documentation, and assessment of ex vivo brain simulation research.
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Affiliation(s)
- 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.
| | - Mohamad Bakhaidar
- 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
| | - Alexander Winkler-Schwartz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Recai Yilmaz
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Rolando F Del Maestro
- Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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17
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Chan J, Pangal DJ, Cardinal T, Kugener G, Zhu Y, Roshannai A, Markarian N, Sinha A, Anandkumar A, Hung A, Zada G, Donoho DA. A systematic review of virtual reality for the assessment of technical skills in neurosurgery. Neurosurg Focus 2021; 51:E15. [PMID: 34333472 DOI: 10.3171/2021.5.focus21210] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 05/19/2021] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Virtual reality (VR) and augmented reality (AR) systems are increasingly available to neurosurgeons. These systems may provide opportunities for technical rehearsal and assessments of surgeon performance. The assessment of neurosurgeon skill in VR and AR environments and the validity of VR and AR feedback has not been systematically reviewed. METHODS A systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted through MEDLINE and PubMed. Studies published in English between January 1990 and February 2021 describing the use of VR or AR to quantify surgical technical performance of neurosurgeons without the use of human raters were included. The types and categories of automated performance metrics (APMs) from each of these studies were recorded. RESULTS Thirty-three VR studies were included in the review; no AR studies met inclusion criteria. VR APMs were categorized as either distance to target, force, kinematics, time, blood loss, or volume of resection. Distance and time were the most well-studied APM domains, although all domains were effective at differentiating surgeon experience levels. Distance was successfully used to track improvements with practice. Examining volume of resection demonstrated that attending surgeons removed less simulated tumor but preserved more normal tissue than trainees. More recently, APMs have been used in machine learning algorithms to predict level of training with a high degree of accuracy. Key limitations to enhanced-reality systems include limited AR usage for automated surgical assessment and lack of external and longitudinal validation of VR systems. CONCLUSIONS VR has been used to assess surgeon performance across a wide spectrum of domains. The VR environment can be used to quantify surgeon performance, assess surgeon proficiency, and track training progression. AR systems have not yet been used to provide metrics for surgeon performance assessment despite potential for intraoperative integration. VR-based APMs may be especially useful for metrics that are difficult to assess intraoperatively, including blood loss and extent of resection.
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Affiliation(s)
- Justin Chan
- 1USC Department of Neurosurgery, Keck School of Medicine of the University of Southern California, Los Angeles, California
| | - Dhiraj J Pangal
- 1USC Department of Neurosurgery, Keck School of Medicine of the University of Southern California, Los Angeles, California
| | - Tyler Cardinal
- 1USC Department of Neurosurgery, Keck School of Medicine of the University of Southern California, Los Angeles, California
| | - Guillaume Kugener
- 1USC Department of Neurosurgery, Keck School of Medicine of the University of Southern California, Los Angeles, California
| | - Yichao Zhu
- 1USC Department of Neurosurgery, Keck School of Medicine of the University of Southern California, Los Angeles, California
| | - Arman Roshannai
- 1USC Department of Neurosurgery, Keck School of Medicine of the University of Southern California, Los Angeles, California
| | - Nicholas Markarian
- 1USC Department of Neurosurgery, Keck School of Medicine of the University of Southern California, Los Angeles, California
| | - Aditya Sinha
- 1USC Department of Neurosurgery, Keck School of Medicine of the University of Southern California, Los Angeles, California
| | - Anima Anandkumar
- 2Computing + Mathematical Sciences, California Institute of Technology, Pasadena, California
| | - Andrew Hung
- 3USC Department of Urology, Keck School of Medicine of the University of Southern California, Los Angeles, California; and
| | - Gabriel Zada
- 1USC Department of Neurosurgery, Keck School of Medicine of the University of Southern California, Los Angeles, California
| | - Daniel A Donoho
- 4Texas Children's Hospital, Baylor College of Medicine, Houston, Texas
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18
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Godzik J, Farber SH, Urakov T, Steinberger J, Knipscher LJ, Ehredt RB, Tumialán LM, Uribe JS. "Disruptive Technology" in Spine Surgery and Education: Virtual and Augmented Reality. Oper Neurosurg (Hagerstown) 2021; 21:S85-S93. [PMID: 34128065 DOI: 10.1093/ons/opab114] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 03/04/2021] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Technological advancements are the drivers of modern-day spine care. With the growing pressure to deliver faster and better care, surgical-assist technology is needed to harness computing power and enable the surgeon to improve outcomes. Virtual reality (VR) and augmented reality (AR) represent the pinnacle of emerging technology, not only to deliver higher quality education through simulated care, but also to provide valuable intraoperative information to assist in more efficient and more precise surgeries. OBJECTIVE To describe how the disruptive technologies of VR and AR interface in spine surgery and education. METHODS We review the relevance of VR and AR technologies in spine care, and describe the feasibility and limitations of the technologies. RESULTS We discuss potential future applications, and provide a case study demonstrating the feasibility of a VR program for neurosurgical spine education. CONCLUSION Initial experiences with VR and AR technologies demonstrate their applicability and ease of implementation. However, further prospective studies through multi-institutional and industry-academic partnerships are necessary to solidify the future of VR and AR in spine surgery education and clinical practice.
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Affiliation(s)
- Jakub Godzik
- Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona, USA
| | - S Harrison Farber
- Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona, USA
| | - Timur Urakov
- Department of Neurosurgery, University of Miami, Miami, Florida, USA
| | - Jeremy Steinberger
- Department of Neurosurgery, Mount Sinai Health System, New York, New York, USA
| | - Liza J Knipscher
- Neuroscience Publications, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona, USA
| | - Ryan B Ehredt
- Neuroscience Publications, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona, USA
| | - Luis M Tumialán
- Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona, USA
| | - Juan S Uribe
- Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona, USA
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Quantitation of Tissue Resection Using a Brain Tumor Model and 7-T Magnetic Resonance Imaging Technology. World Neurosurg 2021; 148:e326-e339. [PMID: 33418122 DOI: 10.1016/j.wneu.2020.12.141] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 12/27/2020] [Accepted: 12/27/2020] [Indexed: 01/17/2023]
Abstract
BACKGROUND Animal brain tumor models can be useful educational tools for the training of neurosurgical residents in risk-free environments. Magnetic resonance imaging (MRI) technologies have not used these models to quantitate tumor, normal gray and white matter, and total tissue removal during complex neurosurgical procedures. This pilot study was carried out as a proof of concept to show the feasibility of using brain tumor models combined with 7-T MRI technology to quantitatively assess tissue removal during subpial tumor resection. METHODS Seven ex vivo calf brain hemispheres were used to develop the 7-T MRI segmentation methodology. Three brains were used to quantitate brain tissue removal using 7-T MRI segmentation methodology. Alginate artificial brain tumor was created in 4 calf brains to assess the ability of 7-T MRI segmentation methodology to quantitate tumor and gray and white matter along with total tissue volumes removal during a subpial tumor resection procedure. RESULTS Quantitative studies showed a correlation between removed brain tissue weights and volumes determined from segmented 7-T MRIs. Analysis of baseline and postresection alginate brain tumor segmented 7-T MRIs allowed quantification of tumor and gray and white matter along with total tissue volumes removed and detection of alterations in surrounding gray and white matter. CONCLUSIONS This pilot study showed that the use of animal tumor models in combination with 7-T MRI technology provides an opportunity to increase the granularity of data obtained from operative procedures and to improve the assessment and training of learners.
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20
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Cornwall GB, Davis A, Walsh WR, Mobbs RJ, Vaccaro A. Innovation and New Technologies in Spine Surgery, Circa 2020: A Fifty-Year Review. Front Surg 2020; 7:575318. [PMID: 33330605 PMCID: PMC7732641 DOI: 10.3389/fsurg.2020.575318] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 10/27/2020] [Indexed: 12/18/2022] Open
Abstract
Spine surgery (lumbar, cervical, deformity, and entire spine) has increased in volume and improved in outcomes over the past 50 years because of innovations in surgical techniques and introduction of new technologies to improve patient care. Innovation is described as a process to add value or create change in an enterprise's economic or social potential. This mini review will assess two of three assessments of innovation in spine surgery: scientific publications and patents issued. The review of both scientific publications and issued patents is a unique assessment. The third assessment of innovation: regulatory clearances of medical devices and equipment for spine surgery and their evolution over time, will also be discussed.
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Affiliation(s)
- G. Bryan Cornwall
- Shiley-Marcos School of Engineering, University of San Diego, San Diego, CA, United States
- Surgical Orthopaedic Research Laboratory, Prince of Wales Hospital, University of New South Wales, Sydney, NSW, Australia
| | | | - William R. Walsh
- Surgical Orthopaedic Research Laboratory, Prince of Wales Hospital, University of New South Wales, Sydney, NSW, Australia
| | - Ralph J. Mobbs
- Surgical Orthopaedic Research Laboratory, Prince of Wales Hospital, University of New South Wales, Sydney, NSW, Australia
| | - Alexander Vaccaro
- Rothman's Orthopaedic Institute, Thomas Jefferson University, Philadelphia, PA, United States
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