Purpose: The aim of this study was to explore the correlations of cone-beam computed tomographic findings with the apnea-hypopnea index in patients with obstructive sleep apnea.
Materials And Methods: Forty patients with obstructive sleep apnea were selected from the ear-nose-throat (ENT) outpatient clinic, Faculty of Medicine, Mansoura University. Cone-beam computed tomography was performed for each patient at the end of both inspiration and expiration. Polysomnography was carried out, and the apnea-hypopnea index was obtained. Linear measurements, including cross-sectional area and the SNA and SNB angles, were obtained. Four oral and maxillofacial radiologists categorized pharyngeal and retropalatal airway morphology and calculated the airway length and volume. Continuous data were tested for normality using the Kolmogorov-Smirnov test and reported as the mean and standard deviation or as the median and range. Categorical data were presented as numbers and percentages, and the significance level was set at <0.05.
Results: The minimal value of the cross-sectional area, SNB angle, and airway morphology at the end of inspiration demonstrated a statistically significant association (<0.05) with the apnea-hypopnea index, with excellent agreement. No statistically significant difference was found in the airway volume, other linear measurements, or retropalatal airway morphology.
Conclusion: Cone-beam computed tomographic measurements in obstructive sleep apnea patients may be used as a supplement to a novel radiographic classification corresponding to the established clinical apnea-hypopnea index classification.
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http://dx.doi.org/10.5624/isd.20230249 | DOI Listing |
Cureus
December 2024
Department of Orthodontics, Kothiwal Dental College and Research Centre, Moradabad, IND.
Introduction The role of the condylar position in the correct functioning of the stomatognathic system has been the center of the study. Using cone-beam computed tomography (CBCT), this study looked at the three-dimensional (3D) position of the condylar bone in patients from Class I, Class II, Division 1, and Division 2. Materials and methods This cross-sectional, retrospective study was conducted using 102 CBCT records, with 34 records allocated to each category of malocclusion classification, such as dentoskeletal Class I, skeletal Class II, and dental Class II, Division 1 and 2.
View Article and Find Full Text PDFClin Cosmet Investig Dent
January 2025
Al-Sabah Center, Al- Yarmouk, Baghdad, Iraq.
Purpose: The study aimed to measure the distance from the cementoenamel junction (CEJ) to the alveolar bone crest on both the buccal and lingual sides of the anterior mandibular teeth utilizing cone beam computed tomography (CBCT).
Materials And Methods: Cone-beam computed tomography (CBCT) was utilized to measure the distance between CEJ and the alveolar bone crest on both the buccal and lingual sides of the mandible's anterior teeth.
Results: The mean of the distance on buccal side for the central, lateral, and canine teeth were (1.
J Esthet Restor Dent
January 2025
Periodontology and Peri-Implant Diseases, Faculty of Medicine and Health Sciences of the University of Barcelona, Barcelona, Spain.
Objective: This study aimed to evaluate the efficacy and safety of a digitally guided dual technique during esthetic crown lengthening surgery. In addition, patient satisfaction and patient-reported outcomes were assessed.
Materials And Methods: A prospective case series study was conducted.
J Dent
January 2025
Department of Biologic and Materials Sciences & Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI, USA. Electronic address:
Objectives: To investigate the influence of different facial scanners and integration approaches on the accuracy of virtual dental patients (VDPs).
Methods: Forty VDPs were generated using a head mannequin and two facial scanners: 1) an industrial scanner and 2) a smartphone scanner. For each scanner, two integration methods were applied: 1) integration by virtual facebow scan and 2) integration by nose-teeth scan.
Clin Oral Investig
January 2025
Department of Periodontology, Semmelweis University, Budapest, Hungary.
Objectives: To investigate the performance of a deep learning (DL) model for segmenting cone-beam computed tomography (CBCT) scans taken before and after mandibular horizontal guided bone regeneration (GBR) to evaluate hard tissue changes.
Materials And Methods: The proposed SegResNet-based DL model was trained on 70 CBCT scans. It was tested on 10 pairs of pre- and post-operative CBCT scans of patients who underwent mandibular horizontal GBR.
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