ArticleEuropean journal of dental education : official journal of the Association for Dental Education in Europe2026
Assessment of Different Technologies in the Fabrication of 3D Printed Tooth Models for Dental Education.
Article in European journal of dental education : official journal of the Association for Dental Education in Europe, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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Authors and funding
6 authors.
Funding
Abstract
background3D printing has emerged as a practical method for fabricating customisable, accurate and affordable dental training models. Numerous 3D printing technologies exist, though there is no standardised comparison to inform their implementation in dental education. This study aimed to evaluate and compare the qualities of various 3D printed dental models against the current standard model used in tertiary dental education. METHODOLOGY: Five maxillary molar tooth models were fabricated using selective laser sintering (SLS), fused filament fabrication (FFF), material jetting (MJ), stereolithography (SLA) and digital light processing (DLP). A standard Ivorine tooth model (One Dental, Australia) was also evaluated. Eighteen participants performed cavity preparations and assessed each model based on haptic feedback, visual appearance, anatomical accuracy and overall suitability using a 5-point Likert scale.
resultsFFF was the most economical per unit ($0.06), followed by DLP ($0.30). MJ was the most expensive ($1.87), though it offers high resolution and complexity. In terms of enamel haptics and dentine haptics, MJ (3.11, 3.28), SLA (3.00, 3.33) and DLP (3.11, 3.44) were assessed as comparable to Ivorine (3.67, 3.44). Similarly, overall suitability of MJ (3.33), SLA (3.33) and DLP (3.44) was comparable to Ivorine (4.17). FFF and SLS were rated significantly inferior to Ivorine in all domains.
conclusionsCurrent 3D printed models do not fully simulate dental tissue and should be used to supplement the limitations of standard models and improve self-learning. Future research is warranted to develop models with improved haptic feedback and realistic surface anatomy.
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