A Review of Physical Simulators for Neuroendoscopy Skills Training.


Journal

World neurosurgery
ISSN: 1878-8769
Titre abrégé: World Neurosurg
Pays: United States
ID NLM: 101528275

Informations de publication

Date de publication:
05 2020
Historique:
received: 03 12 2019
revised: 21 01 2020
accepted: 22 01 2020
pubmed: 6 2 2020
medline: 29 7 2020
entrez: 5 2 2020
Statut: ppublish

Résumé

Minimally invasive neurosurgical approaches reduce patient morbidity by providing the surgeon with better visualization and access to complex lesions, with minimal disruption to normal anatomy. The use of rigid or flexible neuroendoscopes, supplemented with a conventional stereoscopic operating microscope, has been integral to the adoption of these techniques. Neurosurgeons commonly use neuroendoscopes to perform the ventricular and endonasal approaches. It is challenging to learn neuroendoscopy skills from the existing apprenticeship model of surgical education. The training methods, which use simulation-based systems, have achieved wide acceptance. Physical simulators provide anatomic orientation and hands-on experience with repeatability. Our aim is to review the existing physical simulators on the basis of the skills training of neuroendoscopic procedures. We searched Scopus, Google Scholar, PubMed, IEEE Xplore, and dblp. We used the following keywords "neuroendoscopy," "training," "simulators," "physical," and "skills evaluation." A total of 351 articles were screened based on development methods, evaluation criteria, and validation studies on physical simulators for skills training in neuroendoscopy. The screening of the articles resulted in classifying the physical training methods developed for neuroendoscopy surgical skills into synthetic simulators and box trainers. The existing simulators were compared based on their design, fidelity, trainee evaluation methods, and validation studies. The state of simulation systems demands collaborative initiatives among translational research institutes. They need improved fidelity and validation studies for inclusion in the surgical educational curriculum. Learning should be imparted in stages with standardization of performance metrics for skills evaluation.

Sections du résumé

BACKGROUND
Minimally invasive neurosurgical approaches reduce patient morbidity by providing the surgeon with better visualization and access to complex lesions, with minimal disruption to normal anatomy. The use of rigid or flexible neuroendoscopes, supplemented with a conventional stereoscopic operating microscope, has been integral to the adoption of these techniques. Neurosurgeons commonly use neuroendoscopes to perform the ventricular and endonasal approaches. It is challenging to learn neuroendoscopy skills from the existing apprenticeship model of surgical education. The training methods, which use simulation-based systems, have achieved wide acceptance. Physical simulators provide anatomic orientation and hands-on experience with repeatability. Our aim is to review the existing physical simulators on the basis of the skills training of neuroendoscopic procedures.
METHODS
We searched Scopus, Google Scholar, PubMed, IEEE Xplore, and dblp. We used the following keywords "neuroendoscopy," "training," "simulators," "physical," and "skills evaluation." A total of 351 articles were screened based on development methods, evaluation criteria, and validation studies on physical simulators for skills training in neuroendoscopy.
RESULTS
The screening of the articles resulted in classifying the physical training methods developed for neuroendoscopy surgical skills into synthetic simulators and box trainers. The existing simulators were compared based on their design, fidelity, trainee evaluation methods, and validation studies.
CONCLUSIONS
The state of simulation systems demands collaborative initiatives among translational research institutes. They need improved fidelity and validation studies for inclusion in the surgical educational curriculum. Learning should be imparted in stages with standardization of performance metrics for skills evaluation.

Identifiants

pubmed: 32014545
pii: S1878-8750(20)30201-1
doi: 10.1016/j.wneu.2020.01.183
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

398-407

Commentaires et corrections

Type : CommentIn
Type : CommentIn

Informations de copyright

Copyright © 2020. Published by Elsevier Inc.

Auteurs

Britty Baby (B)

Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, India; Amar Nath and Shashi Khosla School of Information Technology, Indian Institute of Technology-Delhi, New Delhi, India.

Ramandeep Singh (R)

Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, India.

Rajdeep Singh (R)

Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, India.

Ashish Suri (A)

Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, India; Amar Nath and Shashi Khosla School of Information Technology, Indian Institute of Technology-Delhi, New Delhi, India. Electronic address: surineuro@gmail.com.

Chetan Arora (C)

Department of Computer Science Engineering, Indian Institute of Technology-Delhi, New Delhi, India.

Subodh Kumar (S)

Department of Computer Science Engineering, Indian Institute of Technology-Delhi, New Delhi, India.

Prem Kumar Kalra (PK)

Department of Computer Science Engineering, Indian Institute of Technology-Delhi, New Delhi, India.

Subhashis Banerjee (S)

Department of Computer Science Engineering, Indian Institute of Technology-Delhi, New Delhi, India.

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