Abstract
Background
Typodont teeth used in preclinical dental training often lack the anatomical fidelity and tactile realism of natural dentition. With advancements in 3D printing, it is now feasible to design and fabricate highly realistic, patient-specific tooth models for educational use.
Methods
A mixed-methods study was conducted with 64 postgraduate dental trainees. Custom 3D-printed tooth models were created using intraoral scans and printed with two material types: Crowntec (CT) and Model 2.0 (M2.0). Participants completed standardised crown preparations and then responded to a validated questionnaire assessing model realism, training effectiveness and user preference. Quantitative responses were analysed using descriptive statistics and open-ended responses were thematically analysed.
Results
CT models were significantly rated higher than M2.0 and standard typodont teeth (STT) in terms of hardness (Ø 4.58 ± 0.75), haptic feedback (Ø 4.48 ± 0.67) and training suitability (Ø 4.56 ± 0.69). Compared to natural teeth, CT models were perceived as realistic in hardness (Ø 3.33 ± 1.07) and tactile feedback (Ø 3.97 ± 0.94). The artificial gingiva mask received moderate ratings in texture (Ø 3.33 ± 0.99) and consistency (Ø 3.5 ± 0.98). Qualitative themes included model design improvements, perceived educational value and cost-effectiveness. Despite a high initial setup cost, CT models were deemed educationally superior.
Conclusion
3D-printed dental models, particularly those using Crowntec resin, offer a realistic, reproducible and effective alternative to traditional models for teaching crown preparation. These models have the potential to improve training outcomes and merit integration into dental curricula.
Typodont teeth used in preclinical dental training often lack the anatomical fidelity and tactile realism of natural dentition. With advancements in 3D printing, it is now feasible to design and fabricate highly realistic, patient-specific tooth models for educational use.
Methods
A mixed-methods study was conducted with 64 postgraduate dental trainees. Custom 3D-printed tooth models were created using intraoral scans and printed with two material types: Crowntec (CT) and Model 2.0 (M2.0). Participants completed standardised crown preparations and then responded to a validated questionnaire assessing model realism, training effectiveness and user preference. Quantitative responses were analysed using descriptive statistics and open-ended responses were thematically analysed.
Results
CT models were significantly rated higher than M2.0 and standard typodont teeth (STT) in terms of hardness (Ø 4.58 ± 0.75), haptic feedback (Ø 4.48 ± 0.67) and training suitability (Ø 4.56 ± 0.69). Compared to natural teeth, CT models were perceived as realistic in hardness (Ø 3.33 ± 1.07) and tactile feedback (Ø 3.97 ± 0.94). The artificial gingiva mask received moderate ratings in texture (Ø 3.33 ± 0.99) and consistency (Ø 3.5 ± 0.98). Qualitative themes included model design improvements, perceived educational value and cost-effectiveness. Despite a high initial setup cost, CT models were deemed educationally superior.
Conclusion
3D-printed dental models, particularly those using Crowntec resin, offer a realistic, reproducible and effective alternative to traditional models for teaching crown preparation. These models have the potential to improve training outcomes and merit integration into dental curricula.
| Original language | English |
|---|---|
| Number of pages | 12 |
| Journal | European Journal of Dental Education |
| Early online date | 18 May 2026 |
| DOIs | |
| Publication status | Early online date - 18 May 2026 |
Keywords
- crown preparation
- simulation‐based learning
- 3D printing
- prosthodontics training
- typodont
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Dive into the research topics of 'The use of 3D‐printed teeth models for crown preparation training: perceptions of postgraduate dental trainees'. Together they form a unique fingerprint.Student theses
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A critical examination and framework development for clinical competence assessment of postgraduate dental specialty trainees
Amir Rad, F. (Author), Morison, S. (Supervisor), McKenna, G. (Supervisor) & Zary, N. (Supervisor), Dec 2026Student thesis: Doctoral Thesis › Doctor of Philosophy
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