Background <p>This study assessed two different 3D-printed endovascular simulation models and a digital simulator for training of endovascular interventions.</p> Methods <p>Thirty-two vascular surgeons and radiologists completed interventions using two transparent 3D-printed models—a flexible model, printed with Stereolithography (SLA), and a stiff model using Fused Deposition Modelling (FDM) technology —as well as a digital simulator. A standardized questionnaire assessed the models’ perceived face and construct validity as well as their concurrent validity. Additionally, the impact of model material (flexible vs. stiff) on perceived fidelity and utility were evaluated.</p> Results <p>All participants completed the three interventions successfully. There was an even distribution of sex (16 males and females) and experience among the participants. The flexible 3D-printed model demonstrated significantly higher face and construct validity scores compared to the stiff model and the digital simulator (<i>p</i> &lt; 0.001). No significant differences were observed between the digital and stiff models for face and construct validity (<i>p</i> = 1.0, <i>p</i> = 0.38). Regarding concurrent validity, there was a significant preference for the 3D-printed models (72% vs. 16%; <i>p</i> &lt; 0.001). The flexible 3D-printed models were strongly favoured (82% vs. 9%; <i>p</i> &lt; 0.001) due to higher scores regarding fidelity of the experienced resistance and tactile response (<i>p</i> &lt; 0.001). Most participants (81%) expressed a desire for regular simulation training.</p> Conclusions <p>Transparent 3D-printed models present a valuable, and potentially superior, alternative to established digital simulators. They achieve higher scores in face and construct validity as well as surpass the digital simulator in concurrent validity. Flexibility emerges as a key factor, significantly enhancing the fidelity and overall training experience of 3D-printed models.</p>

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Evaluation of 3D-printed vs. digital models in simulation-based training for iliac endovascular interventions

  • David Wippel,
  • Miar Ouaret,
  • Inez Ohashi Torres,
  • Michaela Kluckner,
  • Maximilian Lutz,
  • Moritz Wegner,
  • Bernhard Dorweiler,
  • Elke R. Gizewski,
  • Florian K. Enzmann,
  • Sabine Wipper

摘要

Background

This study assessed two different 3D-printed endovascular simulation models and a digital simulator for training of endovascular interventions.

Methods

Thirty-two vascular surgeons and radiologists completed interventions using two transparent 3D-printed models—a flexible model, printed with Stereolithography (SLA), and a stiff model using Fused Deposition Modelling (FDM) technology —as well as a digital simulator. A standardized questionnaire assessed the models’ perceived face and construct validity as well as their concurrent validity. Additionally, the impact of model material (flexible vs. stiff) on perceived fidelity and utility were evaluated.

Results

All participants completed the three interventions successfully. There was an even distribution of sex (16 males and females) and experience among the participants. The flexible 3D-printed model demonstrated significantly higher face and construct validity scores compared to the stiff model and the digital simulator (p < 0.001). No significant differences were observed between the digital and stiff models for face and construct validity (p = 1.0, p = 0.38). Regarding concurrent validity, there was a significant preference for the 3D-printed models (72% vs. 16%; p < 0.001). The flexible 3D-printed models were strongly favoured (82% vs. 9%; p < 0.001) due to higher scores regarding fidelity of the experienced resistance and tactile response (p < 0.001). Most participants (81%) expressed a desire for regular simulation training.

Conclusions

Transparent 3D-printed models present a valuable, and potentially superior, alternative to established digital simulators. They achieve higher scores in face and construct validity as well as surpass the digital simulator in concurrent validity. Flexibility emerges as a key factor, significantly enhancing the fidelity and overall training experience of 3D-printed models.