Background: Digital intraoral scanning, although developing rapidly, is rarely used in occlusal reconstruction. To compensate for the technical drawbacks of current occlusal reconstruction techniques, such as time consumption and high technical requirements, digital intraoral scanning can be used in clinics. This report aims to provide a way of selecting the most suitable maxillo-mandibular relationship (MMR) during recovery.
Case Summary: A 68-year-old man with severely worn posterior teeth underwent occlusal reconstruction with fixed prosthesis using digital intraoral scanning. A series of digital models in different stages of treatment were obtained, subsequently compared, and selected using digital intraoral scanning together with traditional measurements, such as cone beam computed tomography, joint imaging, and clinical examination. Using digital intraoral scanning, the MMR in different stages of treatment was accurately recorded, which provided feasibility for deciding the best occlusal reconstruction treatment, made the treatment process easier, and improved patient satisfaction.
Conclusion: This case report highlights the clarity, recordability, repeatability, and selectivity of digital intraoral scanning to replicate and transfer the MMR during occlusal reconstruction, expanding new perspectives for its design, fabrication, and postoperative evaluation.
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http://dx.doi.org/10.12998/wjcc.v11.i15.3522 | DOI Listing |
Turk J Orthod
December 2024
Çanakkale Onsekiz Mart University Faculty of Dentistry, Department of Orthodontics, Çanakkale, Turkey.
Objective: This study aimed to compare the manufacturing accuracy of different printing techniques - Stereolithography (SLA), Digital Light Processing (DLP), and PolyJet-using digital dental models.
Methods: The study included cast models of 30 patients aged between 12 and 20 years. The selected models were scanned using an intraoral scanner, and surface topography format files were obtained.
J Dent
December 2024
OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium; Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden. Electronic address:
Objectives: To validate a novel artificial intelligence (AI)-based tool for automated tooth modelling by fusing cone beam computed tomography (CBCT)-derived roots with corresponding intraoral scanner (IOS)-derived crowns.
Methods: A retrospective dataset of 30 patients, comprising 30 CBCT scans and 55 IOS dental arches, was used to evaluate the fusion model at full arch and single tooth levels. AI-fused models were compared with CBCT tooth segmentation using point-to-point surface distances-reported as median surface distance (MSD), root mean square distance (RMSD), and Hausdorff distance (HD)- alongside visual assessments.
J Dent
December 2024
Department of Odontology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. Electronic address:
Objectives: To assess the degree of tooth wear in children and adolescents by application of a qualitative wear index and by quantitative measurement on digital models. The hypothesis was that the quantitative method would be sensitive to reliably measure tooth wear.
Methods: Existing digital models (n = 24) gathered from a prospective clinical study were analysed.
Orthod Craniofac Res
December 2024
Department of Orthodontics and Dentofacial Orthopedics, Manav Rachna Dental College, Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India.
Objective: To evaluate the effects of presurgical infant orthopaedics using the Modified Grayson technique and Rhinoplasty Appliance System on the maxillary alveolus and nasolabial region in infants with unilateral cleft lip and palate (UCLP).
Materials And Methods: This prospective study looked at 26 patients with a mean age of 6.3 ± 1.
Sci Rep
December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
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