Objectives: To determine the rate of appropriate requests for panoramic radiography (PR) in a Dental Accident and Emergency Department and the implications for patient dose.
Methods: Two hundred and seventy-one requests for PR during July 1998 were assessed by two dental radiologists and categorised as appropriate or inappropriate based on established selection criteria. Incidental findings that might alter patient management were also noted.
Results: One hundred and fifty-seven requests (58%) were considered appropriate and 114 (42%) inappropriate. The most common inappropriate request was to assess disease localised to one or two teeth. Dental students were involved in 186 requests and 76 of these (41%) were inappropriate. The estimated saving in collective radiation dose over the month of the study if appropriate radiographs had been taken, would have been approximately 540 microSv, a reduction of 70%. Three out of 114 (3%) inappropriate, PRS showed minor incidental findings.
Conclusions: A considerable proportion of requests for PR were inappropriate. In most of these cases, periapical radiographs would have provided more detail with less radiation dose. The large number of inappropriate requests involving dental students has implications for educators. The use of local selection criteria based on currently accepted guidelines would have reduced the dose substantially.
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http://dx.doi.org/10.1038/sj/dmfr/4600628 | DOI Listing |
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 PDFJ Dent Sci
January 2025
School of Medicine, Chung Shan Medical University, Taichung, Taiwan.
BMJ Case Rep
January 2025
Department of Oral and Maxillofacial Surgery, Indira Gandhi Institute of Dental Sciences, Sri Balaji Vidyapeeth (Deemed to be University), Pondicherry, India.
A calcifying epithelial odontogenic tumour (CEOT) is a rare benign odontogenic tumour of epithelial origin accounting for approximately 1% of all odontogenic tumours. The intraosseous form occurs more commonly in the posterior mandible whereas the extraosseous form is common in the anterior maxilla. CEOT is often asymptomatic and presents with a painless swelling of the mandible.
View Article and Find Full Text PDFEur 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.
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.
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