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3D Printing of Biomolecular Models for Research and Pedagogy

3D Printing of Biomolecular Models for Research and Pedagogy

Published on: March 13, 2017

A simple, no-cost method for 3D printed model identification.

C Druelle¹, J Ferri², P Mahy³

¹University of Lille, 42 rue Paul Duez, 59000 Lille, France; Maxillofacial surgery and stomatology department of Lille Univeristy Hospital, hôpital Roger Salengro, rue Emile-Laine, 59037 Lille cedex, France; Maxillofacial surgery and stomatology department, clinique Saint-Luc, CHU de Bruxelles, avenue Hippocrate 10, 1200 Woluwe-Saint-Lambert, Belgium.

Journal of Stomatology, Oral and Maxillofacial Surgery

|November 3, 2019

Summary

This summary is machine-generated.

A new, free method imprints patient identifiers onto 3D printed medical models, preventing mix-ups. Recommended settings ensure clear identification for safer clinical use of 3D printed anatomical models.

Keywords:

3D printing European community Medical devices Medical legislation Patient tracking

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Interactive Molecular Model Assembly with 3D Printing

Interactive Molecular Model Assembly with 3D Printing

Published on: August 13, 2020

Area of Science:

Medical Imaging and 3D Printing
Biomedical Engineering
Digital Health

Background:

3D printed anatomical models share similar shapes, risking patient misidentification.
Accurate patient identification is crucial for the safe and effective use of medical models.
Current identification methods may not be integrated into the 3D model creation process.

Purpose of the Study:

To present a simple, free method for imprinting alphanumeric identifiers onto 3D printed models.
To evaluate the readability and optimal parameters for 3D text identification.
To enhance the safety and traceability of patient-specific 3D printed medical devices.

Main Methods:

CT scan data were processed into an STL file of a patient's mandible.
Blender software was utilized to emboss patient identifiers (letters and numbers) onto the 3D model.
Six models were 3D printed with varying character sizes and depths; readability was assessed by ten readers.

Main Results:

All 3D printed models were successfully created without failures.
Readability testing resulted in only two errors among ten readers.
Optimal parameters identified: 3mm extrusion depth and larger model size were preferred; 7.5mm height recommended.

Conclusions:

The proposed method offers an easy, fast, and free solution for 3D text identification on medical models.
Integrated identification during model creation enhances safety and facilitates digital medical file integration.
This technique represents a significant step towards the regulated clinical application of hospital-generated 3D printed models.