Accurate prediction of facial soft-tissue changes following orthognathic surgery is crucial for improving surgical outcome. However, the accuracy of current prediction methods still requires further improvement in clinically critical regions, especially the lips. We develop a novel incremental simulation approach using finite element method (FEM) with realistic lip sliding effect to improve the prediction accuracy in the area around the lips. First, lip-detailed patient-specific FE mesh is generated based on accurately digitized lip surface landmarks. Second, an improved facial soft-tissue change simulation method is developed by applying a lip sliding effect in addition to the mucosa sliding effect. The soft-tissue change is then simulated incrementally to facilitate a natural transition of the facial change and improve the effectiveness of the sliding effects. A preliminary evaluation of prediction accuracy was conducted using retrospective clinical data. The results showed that there was a significant prediction accuracy improvement in the lip region when the realistic lip sliding effect was applied along with the mucosa sliding effect.
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http://dx.doi.org/10.1007/978-3-030-32254-0_38 | DOI Listing |
Cureus
November 2024
Department of Orthodontics and Dentofacial Orthopaedics, Manav Rachna Dental College, Manav Rachna International Institute of Research and Studies, Faridabad, IND.
Class I bimaxillary protrusion is characterized by proclined incisors, a convex facial profile, procumbent lips, and increased lip strain. Treatment includes the extraction of premolars and the mesial movement of the proclined anterior teeth in the extraction spaces to correct the inclination. This case report describes the treatment of an 18-year-old male patient who presented with class I bimaxillary protrusion and procumbent lips.
View Article and Find Full Text PDFDent J (Basel)
November 2024
Department of Orthodontics, School of Dental Medicine, State University of New York at Buffalo, Buffalo, NY 14214, USA.
BMC Rheumatol
November 2024
Department of Rheumatology, Lausanne University Hospital (CHUV), Lausanne, Switzerland.
Background: The histopathological analysis of minor salivary gland biopsies, particularly through the quantification of the Focus Score (FS), is pivotal in the diagnostic workflow for Sjögren's Syndrome (SS). AI-based image recognition using deep learning models has demonstrated potential in enhancing diagnostic accuracy and efficiency in preclinical research.
Objectives: The primary aim of this investigation was to utilize an auto-machine learning (autoML) platform for the automated segmentation and quantification of FS on histopathological slides, aiming to augment diagnostic precision and speed in SS.
J Perinat Med
October 2024
Department of Obstetrics and Gynecology, Division of Perinatology, 536164 University of Health Sciences, Turkish Ministry of Health Ankara Bilkent City Hospital, Ankara, Türkiye.
Oral Dis
October 2024
Health Care Department, Metropolitan Autonomous University Xochimilco, Mexico City, Mexico.
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