Literature DB >> 31256581

3D printing of aortic models as a teaching tool for improving understanding of aortic disease.

Domenico Spinelli1,2, Stefania Marconi3, Rosario Caruso4, Michele Conti3, Filippo Benedetto5, Hector W De Beaufort6, Ferdinando Auricchio3, Santi Trimarchi7,8.   

Abstract

BACKGROUND: A geometrical understanding of the individual patient's disease morphology is crucial in aortic surgery. The aim of our study was to validate a questionnaire addressing understanding of aortic disease and use this questionnaire to investigate the value of 3D printing as a teaching tool for surgical trainees.
METHODS: Anonymized CT-angiography images of six different patients were selected as didactic cases of aortic disease and made into 3D models of transparent rigid resin with the Vat-photopolymerization technique. The 3D aortic models, which could be disassembled and reassembled, were displayed to 37 surgical trainees, immediately after a seminar on aortic disease. A questionnaire was developed to compare the trainees' understanding before (T0) and after (T1) demonstration of the 3D printed models.
RESULTS: A panel of 15 experts participated in evaluating face and content validity of the questionnaire. The questionnaire validity was established and therefore the information investigated by the questionnaire could be synthetized using the mean of the items to indicate the understanding. The participants (mean age 28 years, range 26-34, male 59%) showed a significant improvement in understanding from T0 (median=7.25; IQR=1.50) to T1 (median=8.00; IQR=1.50; P=0.002).
CONCLUSIONS: Preliminary data suggest that the use of 3D-printed aortic models as a teaching tool was feasible and improved the understanding of aortic disease among surgical trainees.

Entities:  

Year:  2019        PMID: 31256581     DOI: 10.23736/S0021-9509.19.10841-5

Source DB:  PubMed          Journal:  J Cardiovasc Surg (Torino)        ISSN: 0021-9509            Impact factor:   1.888


  1 in total

1.  Preservation of Autologous Brachiocephalic Vessels with Assistance of Three-Dimensional Printing Based on Convolutional Neural Networks.

Authors:  Yu Yan; Yan-Yan Su; Zhong-Ya Yan
Journal:  Comput Math Methods Med       Date:  2022-03-17       Impact factor: 2.238

  1 in total

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