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Abstract
The technology of artificial intelligence (AI) has made significant in-roads into the field of medicine over the last decade. With surgery being a discipline where repetition is the key to mastery, the scope of AI presents enormous potential for resident education through the analysis of technique and delivery of structured feedback for performance improvement. In an era marred by a raging pandemic that has decreased exposure and opportunity, AI offers an attractive solution towards improving operating room efficiency, safe patient care in the hands of supervised residents and can ultimately culminate in reduced health care costs. Through this article, we elucidate the current adoption of the artificial intelligence technology and its prospects for advancing surgical education.
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Affiliation(s)
- David T Guerrero
- 12317University of Pittsburgh Medical School, Pittsburgh, PA, USA
| | - Malke Asaad
- Department of Plastic Surgery, 6595University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Aashish Rajesh
- 14742University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Abbas Hassan
- Department of Plastic Surgery, 571198The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Charles E Butler
- Department of Plastic Surgery, 571198The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Iop A, El-Hajj VG, Gharios M, de Giorgio A, Monetti FM, Edström E, Elmi-Terander A, Romero M. Extended Reality in Neurosurgical Education: A Systematic Review. Sensors (Basel) 2022; 22:6067. [PMID: 36015828 PMCID: PMC9414210 DOI: 10.3390/s22166067] [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] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/06/2022] [Accepted: 08/12/2022] [Indexed: 06/15/2023]
Abstract
Surgical simulation practices have witnessed a rapid expansion as an invaluable approach to resident training in recent years. One emerging way of implementing simulation is the adoption of extended reality (XR) technologies, which enable trainees to hone their skills by allowing interaction with virtual 3D objects placed in either real-world imagery or virtual environments. The goal of the present systematic review is to survey and broach the topic of XR in neurosurgery, with a focus on education. Five databases were investigated, leading to the inclusion of 31 studies after a thorough reviewing process. Focusing on user performance (UP) and user experience (UX), the body of evidence provided by these 31 studies showed that this technology has, in fact, the potential of enhancing neurosurgical education through the use of a wide array of both objective and subjective metrics. Recent research on the topic has so far produced solid results, particularly showing improvements in young residents, compared to other groups and over time. In conclusion, this review not only aids to a better understanding of the use of XR in neurosurgical education, but also highlights the areas where further research is entailed while also providing valuable insight into future applications.
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Affiliation(s)
- Alessandro Iop
- Department of Neurosurgery, Karolinska University Hospital, 141 86 Stockholm, Sweden
- Department of Clinical Neuroscience, Karolinska Institutet, 171 77 Stockholm, Sweden
- KTH Royal Institute of Technology, 114 28 Stockholm, Sweden
| | - Victor Gabriel El-Hajj
- Department of Neurosurgery, Karolinska University Hospital, 141 86 Stockholm, Sweden
- Department of Clinical Neuroscience, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Maria Gharios
- Department of Neurosurgery, Karolinska University Hospital, 141 86 Stockholm, Sweden
- Department of Clinical Neuroscience, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Andrea de Giorgio
- SnT—Interdisciplinary Center for Security, Reliability and Trust, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
| | | | - Erik Edström
- Department of Neurosurgery, Karolinska University Hospital, 141 86 Stockholm, Sweden
- Department of Clinical Neuroscience, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Adrian Elmi-Terander
- Department of Neurosurgery, Karolinska University Hospital, 141 86 Stockholm, Sweden
- Department of Clinical Neuroscience, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Mario Romero
- KTH Royal Institute of Technology, 114 28 Stockholm, Sweden
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Jiang H, Vimalesvaran S, Wang JK, Lim KB, Mogali SR, Car LT. Virtual Reality in Medical Students' Education: Scoping Review. JMIR Med Educ 2022; 8:e34860. [PMID: 35107421 PMCID: PMC8851326 DOI: 10.2196/34860] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/25/2021] [Accepted: 12/30/2021] [Indexed: 05/12/2023]
Abstract
BACKGROUND Virtual reality (VR) produces a virtual manifestation of the real world and has been shown to be useful as a digital education modality. As VR encompasses different modalities, tools, and applications, there is a need to explore how VR has been used in medical education. OBJECTIVE The objective of this scoping review is to map existing research on the use of VR in undergraduate medical education and to identify areas of future research. METHODS We performed a search of 4 bibliographic databases in December 2020. Data were extracted using a standardized data extraction form. The study was conducted according to the Joanna Briggs Institute methodology for scoping reviews and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. RESULTS Of the 114 included studies, 69 (60.5%) reported the use of commercially available surgical VR simulators. Other VR modalities included 3D models (15/114, 13.2%) and virtual worlds (20/114, 17.5%), which were mainly used for anatomy education. Most of the VR modalities included were semi-immersive (68/114, 59.6%) and were of high interactivity (79/114, 69.3%). There is limited evidence on the use of more novel VR modalities, such as mobile VR and virtual dissection tables (8/114, 7%), as well as the use of VR for nonsurgical and nonpsychomotor skills training (20/114, 17.5%) or in a group setting (16/114, 14%). Only 2.6% (3/114) of the studies reported the use of conceptual frameworks or theories in the design of VR. CONCLUSIONS Despite the extensive research available on VR in medical education, there continue to be important gaps in the evidence. Future studies should explore the use of VR for the development of nonpsychomotor skills and in areas other than surgery and anatomy. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.1136/bmjopen-2020-046986.
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Affiliation(s)
- Haowen Jiang
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
| | - Sunitha Vimalesvaran
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
| | - Jeremy King Wang
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
| | - Kee Boon Lim
- School of Biological Sciences, Nanyang Technological University Singapore, Singapore, Singapore
| | | | - Lorainne Tudor Car
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
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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.
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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
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Andersen SAW, Nayahangan LJ, Park YS, Konge L. Use of Generalizability Theory for Exploring Reliability of and Sources of Variance in Assessment of Technical Skills: A Systematic Review and Meta-Analysis. Acad Med 2021; 96:1609-1619. [PMID: 33951677 DOI: 10.1097/acm.0000000000004150] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
PURPOSE Competency-based education relies on the validity and reliability of assessment scores. Generalizability (G) theory is well suited to explore the reliability of assessment tools in medical education but has only been applied to a limited extent. This study aimed to systematically review the literature using G-theory to explore the reliability of structured assessment of medical and surgical technical skills and to assess the relative contributions of different factors to variance. METHOD In June 2020, 11 databases, including PubMed, were searched from inception through May 31, 2020. Eligible studies included the use of G-theory to explore reliability in the context of assessment of medical and surgical technical skills. Descriptive information on study, assessment context, assessment protocol, participants being assessed, and G-analyses was extracted. Data were used to map G-theory and explore variance components analyses. A meta-analysis was conducted to synthesize the extracted data on the sources of variance and reliability. RESULTS Forty-four studies were included; of these, 39 had sufficient data for meta-analysis. The total pool included 35,284 unique assessments of 31,496 unique performances of 4,154 participants. Person variance had a pooled effect of 44.2% (95% confidence interval [CI], 36.8%-51.5%). Only assessment tool type (Objective Structured Assessment of Technical Skills-type vs task-based checklist-type) had a significant effect on person variance. The pooled reliability (G-coefficient) was 0.65 (95% CI, .59-.70). Most studies included decision studies (39, 88.6%) and generally seemed to have higher ratios of performances to assessors to achieve a sufficiently reliable assessment. CONCLUSIONS G-theory is increasingly being used to examine reliability of technical skills assessment in medical education, but more rigor in reporting is warranted. Contextual factors can potentially affect variance components and thereby reliability estimates and should be considered, especially in high-stakes assessment. Reliability analysis should be a best practice when developing assessment of technical skills.
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Affiliation(s)
- Steven Arild Wuyts Andersen
- S.A.W. Andersen is postdoctoral researcher, Copenhagen Academy for Medical Education and Simulation (CAMES), Center for Human Resources and Education, Capital Region of Denmark, and Department of Otolaryngology, The Ohio State University, Columbus, Ohio, and resident in otorhinolaryngology, Department of Otorhinolaryngology-Head & Neck Surgery, Rigshospitalet, Copenhagen, Denmark; ORCID: https://orcid.org/0000-0002-3491-9790
| | - Leizl Joy Nayahangan
- L.J. Nayahangan is researcher, CAMES, Center for Human Resources and Education, Capital Region of Denmark, Copenhagen, Denmark; ORCID: https://orcid.org/0000-0002-6179-1622
| | - Yoon Soo Park
- Y.S. Park is director of health professions education research, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; ORCID: https://orcid.org/0000-0001-8583-4335
| | - Lars Konge
- L. Konge is professor of medical education, University of Copenhagen, and head of research, CAMES, Center for Human Resources and Education, Capital Region of Denmark, Copenhagen, Denmark; ORCID: https://orcid.org/0000-0002-1258-5822
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Pishgar M, Issa SF, Sietsema M, Pratap P, Darabi H. REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health. Int J Environ Res Public Health 2021; 18:ijerph18136705. [PMID: 34206378 PMCID: PMC8296875 DOI: 10.3390/ijerph18136705] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/09/2021] [Accepted: 06/15/2021] [Indexed: 01/04/2023]
Abstract
Introduction: The field of artificial intelligence (AI) is rapidly expanding, with many applications seen routinely in health care, industry, and education, and increasingly in workplaces. Although there is growing evidence of applications of AI in workplaces across all industries to simplify and/or automate tasks there is a limited understanding of the role that AI contributes in addressing occupational safety and health (OSH) concerns. Methods: This paper introduces a new framework called Risk Evolution, Detection, Evaluation, and Control of Accidents (REDECA) that highlights the role that AI plays in the anticipation and control of exposure risks in a worker’s immediate environment. Two hundred and sixty AI papers across five sectors (oil and gas, mining, transportation, construction, and agriculture) were reviewed using the REDECA framework to highlight current applications and gaps in OSH and AI fields. Results: The REDECA framework highlighted the unique attributes and research focus of each of the five industrial sectors. The majority of evidence of AI in OSH research within the oil/gas and transportation sectors focused on the development of sensors to detect hazardous situations. In construction the focus was on the use of sensors to detect incidents. The research in the agriculture sector focused on sensors and actuators that removed workers from hazardous conditions. Application of the REDECA framework highlighted AI/OSH strengths and opportunities in various industries and potential areas for collaboration. Conclusions: As AI applications across industries continue to increase, further exploration of the benefits and challenges of AI applications in OSH is needed to optimally protect worker health, safety and well-being.
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Affiliation(s)
- Maryam Pishgar
- Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60609, USA;
| | - Salah Fuad Issa
- Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA;
| | - Margaret Sietsema
- Environmental and Occupational Health Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA; (M.S.); (P.P.)
| | - Preethi Pratap
- Environmental and Occupational Health Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA; (M.S.); (P.P.)
| | - Houshang Darabi
- Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60609, USA;
- Correspondence:
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Mirchi N, Bissonnette V, Yilmaz R, Ledwos N, Winkler-Schwartz A, Del Maestro RF. The Virtual Operative Assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine. PLoS One 2020; 15:e0229596. [PMID: 32106247 DOI: 10.1371/journal.pone.0229596] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 02/10/2020] [Indexed: 01/16/2023] Open
Abstract
Simulation-based training is increasingly being used for assessment and training of psychomotor skills involved in medicine. The application of artificial intelligence and machine learning technologies has provided new methodologies to utilize large amounts of data for educational purposes. A significant criticism of the use of artificial intelligence in education has been a lack of transparency in the algorithms' decision-making processes. This study aims to 1) introduce a new framework using explainable artificial intelligence for simulation-based training in surgery, and 2) validate the framework by creating the Virtual Operative Assistant, an automated educational feedback platform. Twenty-eight skilled participants (14 staff neurosurgeons, 4 fellows, 10 PGY 4-6 residents) and 22 novice participants (10 PGY 1-3 residents, 12 medical students) took part in this study. Participants performed a virtual reality subpial brain tumor resection task on the NeuroVR simulator using a simulated ultrasonic aspirator and bipolar. Metrics of performance were developed, and leave-one-out cross validation was employed to train and validate a support vector machine in Matlab. The classifier was combined with a unique educational system to build the Virtual Operative Assistant which provides users with automated feedback on their metric performance with regards to expert proficiency performance benchmarks. The Virtual Operative Assistant successfully classified skilled and novice participants using 4 metrics with an accuracy, specificity and sensitivity of 92, 82 and 100%, respectively. A 2-step feedback system was developed to provide participants with an immediate visual representation of their standing related to expert proficiency performance benchmarks. The educational system outlined establishes a basis for the potential role of integrating artificial intelligence and virtual reality simulation into surgical educational teaching. The potential of linking expertise classification, objective feedback based on proficiency benchmarks, and instructor input creates a novel educational tool by integrating these three components into a formative educational paradigm.
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