Evaluation of a 3D‐Printed Model as Complete Case Scenario in Undergraduate Dental Education—Diagnosis, Treatment Planning and Clinical Practice
Year: 2025
Type: article
Abstract: INTRODUCTION: 3D-printed teeth/models are important adjuncts in dental education. Nevertheless, there is a lack of simulated cases to learn and understand complex treatment scenarios, including anamnesis, diagnosis, treatment planning and therapy. MATERIAL AND METHODS: Third-year students (n = 44) received a complete 3D-printed model (upper and lower jaw) of a patient who needed emergency treatment. Based on the information provided (i) general history, (ii) specific dental history, (iii) radiographs, the students made a diagnosis and planned the treatment, which they performed independently under supervision. The case included periodontal, restorative, endodontic and surgical treatments and semi-permanent splinting. Using a 3- or 5-point Likert scale, students rated each treatment episode, the learning outcome and the impact of the model on training using a questionnaire. Chi-square test served for statistical analysis. RESULTS: The majority (63.6%) stated that the interdisciplinary model allowed a better learning effect than the approach dealing with each section separately (p < 0.05). Almost all students rated the diagnostic process as excellent (88.0%-95.5%), as the general history, dental history and radiographs were consistent with the clinical findings. Periodontal therapy was rated significantly lower compared to all other areas (p < 0.05). For endodontic treatment, the students disagreed, abstaining from practising on extracted human teeth. However, the students felt confident to perform all treatment steps in the following clinical courses. CONCLUSION: Customised, interdisciplinary 3D-printed teaching models covering complex treatment strategies were best suited to enhance dental students' skills and foster their enthusiasm for the integrated diagnosis and treatment planning process. Their implementation into dental education is strongly recommended to improve both training and future patient care.
DOI: 10.1111/eje.13100
Institutions: University of Münster, University of Bern
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,330
OpenAlex ID: W4409845833
