Advancing endovascular neurosurgery training with extended reality: opportunities and obstacles for the next decade.

augmented reality endovascular mixed reality neurosurgery virtual reality

Journal

Frontiers in surgery
ISSN: 2296-875X
Titre abrégé: Front Surg
Pays: Switzerland
ID NLM: 101645127

Informations de publication

Date de publication:
2024
Historique:
received: 29 05 2024
accepted: 12 08 2024
medline: 11 9 2024
pubmed: 11 9 2024
entrez: 11 9 2024
Statut: epublish

Résumé

Extended reality (XR) includes augmented reality (AR), virtual reality (VR), and mixed reality (MR). Endovascular neurosurgery is uniquely positioned to benefit from XR due to the complexity of cerebrovascular imaging. Given the different XR modalities available, as well as unclear clinical utility and technical capabilities, we clarify opportunities and obstacles for XR in training vascular neurosurgeons. A systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted. Studies were critically appraised using ROBINS-I. 19 studies were identified. 13 studies used VR, while 3 studies used MR, and 3 studies used AR. Regarding specific educational applications, VR was used for simulation in 10 studies and anatomical modeling in 3 studies. AR was only used for live intra-operative guidance ( Anatomical modeling with VR and MR enhances neurovascular anatomy education with patient-specific, 3-D models from imaging data. AR and MR enable live intra-operative guidance, allowing experienced surgeons to remotely instruct novices, potentially improving patient care and reducing geographic disparities. AR overlays enhance instruction by allowing the surgeon to highlight key procedural aspects during training. Inaccurate tracking of surgical tools is an XR technological barrier for modeling and intra-operative training. Importantly, the most reported application of XR is VR for simulation-using platforms like the Mentice VIST and Angio Mentor. 10 studies examine VR for simulation, showing enhanced procedural performance and reduced fluoroscopy use after short training, although long-term outcomes have not been reported. Early-stage trainees benefited the most. Simulation improved collaboration between neurosurgeons and the rest of the surgical team, a promising role in interprofessional teamwork. Given the strength of VR for simulation, MR for simulation is an important gap in the literature for future studies. In conclusion, XR holds promise for transforming neurosurgical education and practice for simulation, but technological research is needed in modeling and intra-procedural training.

Sections du résumé

Background UNASSIGNED
Extended reality (XR) includes augmented reality (AR), virtual reality (VR), and mixed reality (MR). Endovascular neurosurgery is uniquely positioned to benefit from XR due to the complexity of cerebrovascular imaging. Given the different XR modalities available, as well as unclear clinical utility and technical capabilities, we clarify opportunities and obstacles for XR in training vascular neurosurgeons.
Methods UNASSIGNED
A systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted. Studies were critically appraised using ROBINS-I.
Results UNASSIGNED
19 studies were identified. 13 studies used VR, while 3 studies used MR, and 3 studies used AR. Regarding specific educational applications, VR was used for simulation in 10 studies and anatomical modeling in 3 studies. AR was only used for live intra-operative guidance (
Conclusions UNASSIGNED
Anatomical modeling with VR and MR enhances neurovascular anatomy education with patient-specific, 3-D models from imaging data. AR and MR enable live intra-operative guidance, allowing experienced surgeons to remotely instruct novices, potentially improving patient care and reducing geographic disparities. AR overlays enhance instruction by allowing the surgeon to highlight key procedural aspects during training. Inaccurate tracking of surgical tools is an XR technological barrier for modeling and intra-operative training. Importantly, the most reported application of XR is VR for simulation-using platforms like the Mentice VIST and Angio Mentor. 10 studies examine VR for simulation, showing enhanced procedural performance and reduced fluoroscopy use after short training, although long-term outcomes have not been reported. Early-stage trainees benefited the most. Simulation improved collaboration between neurosurgeons and the rest of the surgical team, a promising role in interprofessional teamwork. Given the strength of VR for simulation, MR for simulation is an important gap in the literature for future studies. In conclusion, XR holds promise for transforming neurosurgical education and practice for simulation, but technological research is needed in modeling and intra-procedural training.

Identifiants

pubmed: 39258246
doi: 10.3389/fsurg.2024.1440228
pmc: PMC11385296
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

1440228

Informations de copyright

© 2024 Patel, Covell, Patel, Kandregula, Palepu, Gajjar, Shekhtman, Sioutas, Dhanaliwala, Gade, Burkhardt and Srinivasan.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Auteurs

Shray A Patel (SA)

Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, United States.

Michael M Covell (MM)

School of Medicine, Georgetown University, Washington, DC, United States.

Saarang Patel (S)

Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.

Sandeep Kandregula (S)

Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.

Sai Krishna Palepu (SK)

Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.

Avi A Gajjar (AA)

Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.

Oleg Shekhtman (O)

Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.

Georgios S Sioutas (GS)

Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.

Ali Dhanaliwala (A)

Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.

Terence Gade (T)

Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.

Jan-Karl Burkhardt (JK)

Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.

Visish M Srinivasan (VM)

Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.

Classifications MeSH