Evidence map›Paper›PMID 39399349›Full record

ArticleWorld neurosurgery: X2024

Validation of a dynamic 4D microsurgical bypass simulator for training and teaching microvascular anastomosis techniques with blood flow and fluorescence imaging.

Hanne Eline R Vanluchene, David Bervini, Ross Straughan, Samuel Maina, Fredrick J Joseph

Abstract read
In one paragraph

Article in World neurosurgery: X, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Hanne Eline R VanlucheneARTORG center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
David BerviniDepartment of Neurosurgery, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Ross StraughanARTORG center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Samuel MainaARTORG center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Fredrick J JosephARTORG center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Microvascular anastomosis is challenging, and training surgeons to develop and maintain skills is imperative. Current training models either miss the simulation of the surgical workflow, lack 3D key-hole space, need ethical approval, require special preparation, or lack realism. To circumvent these issues, this study describes the use of a mixed reality 3D printed model with integrated blood flow for training cerebral anastomosis and assesses its validity. Methods: Different-sized 3D-printed artificial micro artery models in a 3D brain space with a keyhole opening were used. The model was connected to a 4D simulator to induce pulsatile blood flow. Neurosurgical microscopes and exoscopes were used for visualization. Nine participants (n = 6 board-certified cerebrovascular neurosurgeons; n = 3 in-training) participated in the study and practiced anastomosis techniques with the simulator. Two senior, experienced vascular neurosurgeons mentored live teaching activity on the simulator. Participants completed a survey to score the face and content validity of the simulation on a 5-point Likert scale. Simulation time and anastomosis score differences between in-training and board-certified participants were compared for construct validity. Results: Participants scored the simulation difficulty similar to actual surgery, proving face validity. All participants agreed that practice on the provided simulator models would improve bypass techniques ( Conclusions: The 4D simulator and the high-fidelity artificial 3D printed model effectively simulated actual bypass surgery in a key-hole fashion with blood flow abilities. Limited resources and preparation time are needed for the training setup. The model provides benefits in learning and maintaining anastomosis skills and allows for easy adaptation to different microanatomical scenarios.

Indexed as

Cerebral bypassMicrovascular anastomosisNeurosurgical trainingSurgical simulation

Identifiers

PMID39399349
PMCPMC11470790

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.