Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Evaluation of angular and positional changes of maxillary third molars following premolar extraction, molar distalization, and non-extraction treatments.
BMC Oral Health
January 2025
Department of Orthodontics, Afyonkarahisar Health Sciences University Faculty of Dentistry, Afyonkarahisar, Turkey.
Background: To compare the effects of first premolar extraction, molar distalization, and non-extraction treatments on the angulation and vertical positions of maxillary second molars (MxM2s) and maxillary third molars (MxM3s). To our knowledge, this is the first study to compare the effects of three different treatment types on MxM3 simultaneously.
Methods: Initial (T0) and final (T1) panoramic radiographs of three different patient groups were analyzed: first premolar extraction group (n = 26 patients, 52 MxM2, 52 MxM3), molar distalization group (n = 20 patients, 40 MxM2, 40 MxM3), and non-extraction group (n = 31 patients, 62 MxM2, 62 MxM3).
Enhancing furcation involvement classification on panoramic radiographs with vision transformers.
BMC Oral Health
January 2025
Department of Periodontics, Affiliated Hospital of Medical School, Nanjing Stomatological Hospital, Research Institute of Stomatology, Nanjing University, Nanjing, China.
Background: The severity of furcation involvement (FI) directly affected tooth prognosis and influenced treatment approaches. However, assessing, diagnosing, and treating molars with FI was complicated by anatomical and morphological variations. Cone-beam computed tomography (CBCT) enhanced diagnostic accuracy for detecting FI and measuring furcation defects.
View Article and Find Full Text PDFPeriapical bone edema volume in 3D MRI is positively correlated with bone architecture changes.
Insights Imaging
January 2025
Diagnostic and Interventional Radiology, University Hospital of Zurich, University Zurich, Zurich, Switzerland.
Objectives: To compare and correlate bone edema volume detected by 3D-short-tau-inversion-recovery (STIR) sequence to osseous decay detected by a T1-based sequence and conventional panoramic radiography (OPT).
Materials And Methods: Patients with clinical evidence of apical periodontitis were included retrospectively and received OPT as well as MRI of the viscerocranium including a 3D-STIR and a 3D-T1 gradient echo sequence. Bone edema was visualized using the 3D-STIR sequence and periapical hard tissue changes were evaluated using the 3D-T1 sequence.
Optimizing dental implant identification using deep learning leveraging artificial data.
Sci Rep
January 2025
Department of Oral and Maxillofacial Surgery, Faculty of Medicine, Kagawa University, 1750-1, Ikenobe, Miki-cho, Kita-gun, Takamatsu, 761-0793, Kagawa, Japan.
This study aims to evaluate the potential enhancement in implant classification performance achieved by incorporating artificially generated images of commercially available products into a deep learning process of dental implant classification using panoramic X-ray images. To supplement an existing dataset of 7,946 in vivo dental implant images, a three-dimensional scanner was employed to create implant surface models. Subsequently, implant surface models were used to generate two-dimensional X-ray images, which were compiled along with original images to create a comprehensive dataset.
View Article and Find Full Text PDFImpacted lower third molar classification and difficulty index assessment: comparisons among dental students, general practitioners and deep learning model assistance.
BMC Oral Health
January 2025
Bangkok Hospital Dental Center Holistic Care and Dental Implant, Bangkok Hospital, Bangkok, 10310, Thailand.
Background: Assessing the difficulty of impacted lower third molar (ILTM) surgical extraction is crucial for predicting postoperative complications and estimating procedure duration. The aim of this study was to evaluate the effectiveness of a convolutional neural network (CNN) in determining the angulation, position, classification and difficulty index (DI) of ILTM. Additionally, we compared these parameters and the time required for interpretation among deep learning (DL) models, sixth-year dental students (DSs), and general dental practitioners (GPs) with and without CNN assistance.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!