Evidence map›Paper›PMID 36046734›Full record

ArticleMethodsX2022

Modelling and fabrication procedure for a 3D printed cardiac model - surgical planning of Left Ventricular Aneurysm.

Francesco Buonamici, Elisa Mussi, Chiara Santarelli, Nazario Carrabba, Pierluigi Stefano, Niccolò Marchionni, Monica Carfagni

Abstract read
In one paragraph

Article in MethodsX, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Francesco BuonamiciDepartment of industrial Engineering of Florence, Italy.
Elisa MussiDepartment of industrial Engineering of Florence, Italy.
Chiara SantarelliDepartment of industrial Engineering of Florence, Italy.
Nazario CarrabbaDepartment Cardiothoracovascular, Careggi Hospital, Italy.
Pierluigi StefanoDepartment Cardiothoracovascular, Careggi Hospital, Italy.
Niccolò MarchionniDepartment Cardiothoracovascular, Careggi Hospital, Italy.
Monica CarfagniDepartment of industrial Engineering of Florence, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The present paper describes a procedure for the development and production of a physical model for surgical planning of a Left Ventricular Aneurysm. The method is based on the general approach provided in Otton et al. (2017) and was customized to seek a reliable and fast procedure for the production of a specific type of cardiac model - i.e. chambers of the left side of the heart. The paper covers all the steps: processing of the data, segmentation, modelling and 3D printing; details are provided for all the phases, in order to allow the reproduction of the achieved results. The procedure relies on Computed Tomography - CT imaging as data source for the identification and modelling of the anatomy. Materialise Mimics was used as segmentation software to process the CT data. While its usefulness for the surgical needs was verified on a single clinical case (provided by the Careggi Hospital of Florence, Italy), the modelling procedure was tested twice, to produce a physical replica both ex-ante and ex-post surgical intervention.•The tools used for segmentation and generation of the printable model were customized to reduce modelling time for the specific type of desired model.•Detailed information on the use of modeling tools, not available in the literature, will be provided.•The procedure allows fabrication of a physical model representing the heart chambers in a short time.

Indexed as

3D printingLeft Ventricular AneurysmSurgical planning

Identifiers

PMID36046734
PMCPMC9421386

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.