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Updated: Sep 19, 2025

Creation of a High-Fidelity, Low-Cost, Intraosseous Line Placement Task Trainer via 3D Printing
Published on: August 17, 2022
[Intraosseous access in infants-development of an anatomical training model]
Dietrich Stoevesandt1, Lina Woydt2, Joachim Koppenberg3
1Dorothea-Erxleben-Lernzentrum, Medizinische Fakultät, Martin-Luther-Universität Halle-Wittenberg, Magdeburger Straße 12, 06112, Halle (Saale), Deutschland. dietrich.stoevesandt@medizin.uni-halle.de.
A new 3D printed training model for intraosseous (i.o.) punctures in infants is cost-effective and anatomically accurate. This model addresses high misplacement rates by offering a realistic simulation for emergency medicine training.
Area of Science:
- Pediatric emergency medicine
- Medical simulation and training
- Anatomical modeling
Context:
- Intraosseous (i.o.) access is a critical emergency procedure for infants and young children.
- High rates of i.o. access misplacement highlight a need for improved training.
- Current training methods lack sufficient opportunities and realistic simulation.
Purpose:
- To analyze malpunctures in pediatric intraosseous access using postmortem CT imaging.
- To develop and evaluate a cost-effective, 3D-printed training model for infant i.o. punctures.
- To assess the anatomical accuracy and user-perceived suitability of the developed training model.
Summary:
- Retrospective analysis of 25 pediatric postmortem CT scans revealed a 40% malpuncture rate in i.o. access, often due to poor anatomical assessment.
- A three-part, 3D-printed and silicone-molded training model was developed based on malpuncture analysis.
- Fifty-five experienced users rated the model as suitable for beginner training, suggesting improvements in haptic feedback and loss of resistance simulation.
Impact:
- The 3D printed model provides an affordable and anatomically precise training tool for intraosseous punctures in infants.
- This model has the potential to enhance user competence and safety in emergency i.o. access procedures.
- Future iterations can improve haptic feedback and puncture success simulation for more effective training.
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