A novel ex vivo trainer for robotic vesicourethral anastomosis.


Journal

Journal of robotic surgery
ISSN: 1863-2491
Titre abrégé: J Robot Surg
Pays: England
ID NLM: 101300401

Informations de publication

Date de publication:
Feb 2020
Historique:
received: 28 11 2018
accepted: 18 01 2019
pubmed: 29 1 2019
medline: 20 8 2020
entrez: 29 1 2019
Statut: ppublish

Résumé

Robotic surgical skill development is central to training in urology as well as other surgical disciplines. Vesicourethral anastomosis (VUA) in robotic prostatectomy is a challenging task for novices due to delicate tissue and difficult suturing angles. Commercially available, realistic training models are limited. Here, we describe the development and validation of a 3D-printed model of the VUA for ex vivo training using the da Vinci Surgical System. Models of the bladder and urethra were created using 3D-printing technology based on estimations of average in vivo anatomy. 10 surgical residents without prior robotics training were enrolled in the study: 5 residents received structured virtual reality (VR) training on the da Vinci Skills Simulator ("trained"), while the other 5 did not ("untrained"). 4 faculty robotic surgeons trained in robotic urologic oncology ("experts") were also enrolled. Mean (range) completion percentage was 20% (10-30%), 54% (40-70%), and 96% (85-100%) by the untrained, trained, and expert groups, respectively. Anastomosis integrity was rated as excellent (as opposed to moderate or poor) in 40%, 60%, and 100% of untrained, trained, and expert groups, respectively. Face validity (realism) was rated as 8 of 10 on average by the expert surgeons, each of whom rated the model as a superior training tool to digital VR trainers. Content validity (usefulness) was rated as 10 of 10 by all participants. This is the first reported 3D-printed ex vivo trainer for VUA in robotic prostatectomy validated for use in robotic simulation. The addition of 3D-printed ex vivo training to existing digital simulation technologies may augment and improve robotic surgical education in the future.

Identifiants

pubmed: 30689167
doi: 10.1007/s11701-019-00926-1
pii: 10.1007/s11701-019-00926-1
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

21-27

Subventions

Organisme : Intuitive Surgical
ID : Intuitive Surgical Standalone Simulator Program

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Auteurs

Kevin Shee (K)

Geisel School of Medicine at Dartmouth, Hanover, NH, USA. Kevin.Shee.MED@dartmouth.edu.
One Medical Center Drive Lebanon, Lebanon, NH, 03756, USA. Kevin.Shee.MED@dartmouth.edu.

Kevin Koo (K)

Section of Urology, Department of Surgery, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA.

Xiaotian Wu (X)

Thayer School of Engineering at Dartmouth, Hanover, NH, USA.

Fady M Ghali (FM)

Geisel School of Medicine at Dartmouth, Hanover, NH, USA.

Ryan J Halter (RJ)

Thayer School of Engineering at Dartmouth, Hanover, NH, USA.

Elias S Hyams (ES)

Section of Urology, Department of Surgery, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA.

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Classifications MeSH