Objectives: The aim of this study was to compare the performance of cone beam computed tomography (CBCT) and digital radiography in the detection of artificial recurrent caries-like lesions under amalgam and composite fillings.
Study Design: The study included class II cavities in 30 molars that had been filled with amalgam. Fifteen of those molars had the restoration-enamel interface artificially demineralized. Phantoms were prepared, and CBCT images were acquired with 2 units in 3 voxel sizes (K9000, 0.076 mm; i-CAT, 0.2 mm and 0.4 mm). Intraoral radiographs were obtained with 3 systems (Digora, VistaScan, and RVG-6100). Amalgam fillings were then replaced by composite, and new images were obtained. Three examiners assessed all of the images. Sensitivity, specificity, accuracy, and receiver operating characteristic curve were calculated and verified through analysis of variance and the Tukey test.
Results: There were no significant differences in sensitivity and specificity when the same restorative material was present or when the restorative materials were compared with the imaging technique as a constant. As for accuracy and receiver operating characteristic curve, there were statistically significant differences when the 2 materials were compared, and there were differences in the amalgam group when the imaging modalities were compared.
Conclusions: CBCT performed similarly to intraoral radiography in detecting demineralization under restorations. However, the voxel size and the type of restorative material influenced its performance.
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http://dx.doi.org/10.1016/j.oooo.2017.05.469 | 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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