Objective: To compare the measured values obtained from the plaster model, digital models created by scanning the plaster models and direct intraoral scanning with the values obtained from direct intraoral measurements.
Design: This was a prospective clinical study.
Setting: The study was conducted in Department of Orthodontics, Saveetha Dental College and Hospital, Tamil Nadu, India.
Participants: Ten patients before the start of orthodontic treatment were selected for the study.
Methods: A computer-aided design and manufacturing (CAD-CAM) system is an advanced technology that is being adopted in the field of orthodontics for diagnosis, treatment planning and documentation of patient records. Mesiodistal tooth width measurements of first premolars, canines, lateral incisors and central incisors, and transverse width measurement from mesial pit of right first premolar to mesial pit of left first premolar in both maxilla and mandible were obtained from direct intraoral measurement (gold standard), study model obtained from alginate impression, intraoral scanned image, and desktop scanned image of the study model. Descriptive statistics and ANOVA was performed to find the difference in mean among the groups.
Results: A value > 0.05 was obtained in ANOVA indicating that there is no statistically significant difference in the measurements obtained by either of the methods.
Conclusion: Conventional stone models and digital models obtained from intraoral scan and desktop scanning of plaster models are clinically reliable as the variations in measurements obtained from these methods were clinically negligible.
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http://dx.doi.org/10.1177/1465312520910755 | DOI Listing |
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.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Computer Science and Digital Technologies, University of East London, London, UK.
Nursing activity recognition has immense importance in the development of smart healthcare management and is an extremely challenging area of research in human activity recognition. The main reasons are an extreme class-imbalance problem and intra-class variability depending on both the subject and the recipient. In this paper, we apply a unique two-step feature extraction, coupled with an intermediate feature 'Angle' and a new feature called mean min max sum to render the features robust against intra-class variation.
View Article and Find Full Text PDFActa Otolaryngol
December 2024
Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Background: There is a lack of prognosticators of overall survival (OS) for Oral Squamous Cell Carcinoma (OSCC).
Objectives: We examined collaborative machine learning (cML) in estimating the OS of OSCC patients. The prognostic significance of the clinicopathological parameters was examined.
JMIR Rehabil Assist Technol
December 2024
Centre de recherche interdisciplinaire en réadaptation du Montréal métropolitain (CRIR) - Institut universitaire sur la réadaptation en déficience physique de Montréal (IURDPM) du Centre intégré universitaire de santé et de services sociaux du Centre-Sud-de-l'Île-de-Montréal (CCSMTL), Université de Montréal, Institut de Réadaptation Gingras Lindsay de Montréal, 6300 avenue de Darlington, Montréal, QC, H3S 2J4, Canada, 1 514-343-6111.
Background: Stationary bikes are used in numerous rehabilitation settings, with most offering limited functionalities and types of training. Smart technologies, such as artificial intelligence and robotics, bring new possibilities to achieve rehabilitation goals. However, it is important that these technologies meet the needs of users in order to improve their adoption in current practice.
View Article and Find Full Text PDFJ Exp Orthop
January 2025
Department of Orthopedic Surgery and Traumatology, Freiburg University Hospital Albert Ludwigs University Freiburg Freiburg Germany.
Introduction: The medial patellofemoral ligament (MPFL) is the main patellar stabilizer in low knee flexion degrees (0-30°). Isolated MPFL reconstruction (MPFLr) is therefore considered the gold standard of surgical procedures for low flexion patellofemoral instabilities (PFIs). Despite excellent clinical results, little is known about the effect of MPFLr on kinematic parameters (KPs) of the patellofemoral joint in vivo.
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