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Deep learning for forensic age estimation using orthopantomograms in children, adolescents, and young adults.
Eur Radiol
January 2025
Department of Radiology, Jena University Hospital-Friedrich Schiller University, Am Klinikum 1, 07747, Jena, Germany.
Objectives: Forensic age estimation from orthopantomograms (OPGs) can be performed more quickly and accurately using convolutional neural networks (CNNs), making them an ideal extension to standard forensic age estimation methods. This study evaluates improvements in forensic age prediction for children, adolescents, and young adults by training a custom CNN from a previous study, using a larger, diverse dataset with a focus on dental growth features.
Methods: 21,814 OPGs from 13,766 individuals aged 1 to under 25 years were utilized.
Clinically oriented automatic three-dimensional enamel segmentation via deep learning.
BMC Oral Health
January 2025
Center of Digital Dentistry, Peking University School and Hospital of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, No.22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, PR China.
Background: Establishing accurate, reliable, and convenient methods for enamel segmentation and analysis is crucial for effectively planning endodontic, orthodontic, and restorative treatments, as well as exploring the evolutionary patterns of mammals. However, no mature, non-destructive method currently exists in clinical dentistry to quickly, accurately, and comprehensively assess the integrity and thickness of enamel chair-side. This study aims to develop a deep learning work, 2.
View Article and Find Full Text PDFCan Viewing Modality Affect Frontal Mandibular Bone Height Measurement? A Comparison Between 3D Digital Imaging and Communications in Medicine Viewer and Printed Portable Document Format Cone Beam Computer Tomography Reports.
Dent J (Basel)
January 2025
Departments of Endodontics, Israel Defense Forces (IDF), Medical Corps, Tel Hashomer Medical Center, Ramat Gan 52621, Israel.
Buccal cortical bone dimensions are crucial in dental radiology, as they impact orthodontic treatment outcomes. Changes in alveolar bone dimensions can result in malocclusion and require interdisciplinary approaches for correction. The accurate quantification of buccal bone dimensions is crucial for appropriate treatment planning and avoiding medico-legal issues.
View Article and Find Full Text PDFArtificial Intelligence-Based Detection and Numbering of Dental Implants on Panoramic Radiographs.
Clin Implant Dent Relat Res
February 2025
SEMRUK Technology Inc., Cumhuriyet Teknokent, Sivas, Turkiye.
Objectives: This study aimed to develop an artificial intelligence (AI)-based deep learning model for the detection and numbering of dental implants in panoramic radiographs. The novelty of this model lies in its ability to both detect and number implants, offering improvements in clinical decision support for dental implantology.
Materials And Methods: A retrospective dataset of 32 585 panoramic radiographs, collected from patients at Sivas Cumhuriyet University between 2014 and 2024, was utilized.
Classification and prognostic evaluation of ameloblastoma using multiplanar CT imaging: a retrospective analysis.
BMC Oral Health
January 2025
Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, Republic of Korea.
Background: Ameloblastoma is the most prevalent odontogenic tumor of the jaw, with a significant recurrence rate. It was conventionally classified radiographically as unilocular or multilocular. As 3D images become more common, there is a need to reassess this classification.
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