Background: Few studies have been reported regarding the accuracy of 3D-printed models for orthodontic applications. The aim of this study was to assess the accuracy of 3D-printed dental models of different tooth surfaces.
Methods: Thirty volunteers were recruited from the hospital, and then their dental models were produced by means of oral scanning and a stereolithography-based 3D printer. Each printed model was digitally scanned and compared with the oral-scanned STL file via superimposition analysis. A color map was used to assess the accuracy of different surfaces (occlusal, buccal, lingual) of anterior and posterior teeth. The Tukey test was used to evaluate the differences between the superimposition.
Results: Statistically significant differences were found in the average deviations of different tooth surfaces (P < 0.05). The mean average absolute deviations of the occlusal surfaces of posterior teeth were greater than those of other surfaces. Percentages of points beyond the upper and lower limits of different tooth surfaces displayed the same results (P < 0.05).
Conclusions: Occlusal surfaces, especially pits and fissures of posterior teeth on 3D printed maxillary dental models, showed greater distortions than those of other teeth and regions.
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http://dx.doi.org/10.1186/s12903-020-01338-6 | DOI Listing |
PLoS One
January 2025
Nova School of Business and Economics, Universidade Nova de Lisboa, Carcavelos, Portugal.
This empirical study assessed the potential of developing a machine-learning model to identify children and adolescents with poor oral health using only self-reported survey data. Such a model could enable scalable and cost-effective screening and targeted interventions, optimizing limited resources to improve oral health outcomes. To train and test the model, we used data from 2,133 students attending schools in a Portuguese municipality.
View Article and Find Full Text PDFDent Traumatol
January 2025
Division of Orthodontics and Dentofacial Deformities, Centre for Dental Education and Research, All India Institute of Medical Sciences, Delhi, India.
Background/aims: Preformed zirconia crowns have emerged as the preferred choice for restoring damaged primary incisors. However, they differ from natural teeth in their biophysical properties and can potentially alter the overall response of crowned teeth to a traumatic load. This in silico study aimed to compare the response of three different traumatic loading conditions for the (i) natural (M1) and (ii) zirconia-restored tooth models (M2) models.
View Article and Find Full Text PDFDent Traumatol
January 2025
Department of Pediatric Dentistry, Dentistry Faculty, Bolu Abant İzzet Baysal University, Bolu, Turkey.
Background/aim: The use of AI-driven chatbots for accessing medical information is increasingly popular among educators and students. This study aims to assess two different ChatGPT models-ChatGPT 3.5 and ChatGPT 4.
View Article and Find Full Text PDFCongenit Anom (Kyoto)
January 2025
Division of Research and Treatment for Oral and Maxillofacial Congenital Anomalies, School of Dentistry, Aichi Gakuin University, Nagoya, Japan.
Pregnancy loss is a significant concern worldwide, encompassing miscarriage and stillbirth. Miscarriage, defined as the loss of a baby before 28 weeks of gestation, accounts for approximately 15% of pregnancies. Stillbirth, occurring at or after 28 weeks of gestation, affects nearly 2.
View Article and Find Full Text PDFInt J Legal Med
January 2025
Centro de Estatística e Aplicações Universidade de Lisbao, CEAUL, Faculdade de Ciências da Universidade de Lisboa no Bloco C6 - Piso 4, Lisboa, 1749-016, Portugal.
Introduction: In the reconstructive phase of medico-legal human identification, the sex estimation is crucial in the reconstruction of the biological profile and can be applied both in identifying victims of mass disasters and in the autopsy room. Due to the inherent subjectivity associated with traditional methods, artificial intelligence, specifically, convolutional neural networks (CNN) may present a competitive option.
Objectives: This study evaluates the reliability of VGG16 model as an accurate forensic sex prediction algorithm and its performance using orthopantomography (OPGs).
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