Objectives: Bitewing radiographs are mainly used to confirm clinical findings in caries diagnostics. The objective here was to investigate the quality of bitewing radiographs after short brush-up training and additional findings besides caries in a low-caries population.
Methods: The material of this cross-sectional study comprised 377 pairs of bitewing radiographs of 19- to 20-year-olds taken by dentists. Radiography was considered indicated if one dentinal caries lesion was present on clinical examination. A senior oral radiologist evaluated quality and diagnosed the findings afterwards unaware of clinical status. The association between variables was analysed using cross tabulation and chi-squared testing.
Results: Almost half of the images were of compromised quality (44.1%). Dentinal caries lesions were detected in 82.3% and enamel lesions in 73.5% of the subjects. On average, the subjects had 1.7 (SD 0.52) dentinal lesions. Fillings were found in 81.8%, fractures/cracks in 11.7%, and attrition in 7.4% of the subjects. Signs of excessive bite force were recorded in 19.4%, whereas marginal bone loss was detected in 6.4%. No significant correlation was detected between fractures, attrition, and excessive bite forces.
Conclusions: Effort must be taken to ensure high quality of bitewing radiographs. In addition to caries detection, bitewing radiographs offer additional value, such as detecting excessive bite forces, tooth wear, and marginal bone loss among young adults.
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http://dx.doi.org/10.1155/2021/8894917 | DOI Listing |
Children (Basel)
November 2024
Department of Pediatric Dentistry, Faculty of Dental Medicine, Hebrew University, Hadassah Medical Center, P.O. Box 12272, Jerusalem 91120, Israel.
Objectives: The present prospective study aimed to compare near-infrared light reflection (NIRI) and bitewing radiographs (BWR) images to detect proximal caries in primary teeth.
Methods: 71 children underwent routine BWR, and scans were performed using an intra-oral scanner (iTero Element 5D, Align Technology, Tempe, AZ, USA), including a near-infrared light source (850 nm) and sensor. Five specialist pediatric dentists examined the NIRI and BWR images.
Braz Dent J
December 2024
Graduate Program in Dentistry, Dental School, Federal University of Pelotas, Pelotas, Brazil.
The combination of different methods has been advocated to increase sensitivity in detecting secondary caries lesions. This cross-sectional study compared the detection of caries lesions around posterior restorations and treatment decisions using bitewing radiographs alone or in combination with clinical information from patient records. The radiographs (n = 212) were randomly distributed into two sequences for assessment across two phases, with a wash-out period of two weeks.
View Article and Find Full Text PDFCureus
November 2024
Pediatric Dentistry, Security Forces Hospital, Mecca, SAU.
Odontomas are the most prevalent odontogenic tumors, often classified as hamartomas due to their slow growth and non-aggressive nature. Typically asymptomatic, they can obstruct the eruption of adjacent teeth. While the exact causes of odontomas remain unclear, potential factors include local trauma, infection, growth pressure, and hereditary influences.
View Article and Find Full Text PDFDentomaxillofac Radiol
November 2024
Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices& Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials, Beijing, China.
Clin Exp Dent Res
December 2024
Melbourne Dental School, The University of Melbourne, Carlton, Victoria, Australia.
Objectives: Artificial intelligence (AI) is an emerging field in dentistry. AI is gradually being integrated into dentistry to improve clinical dental practice. The aims of this scoping review were to investigate the application of AI in image analysis for decision-making in clinical dentistry and identify trends and research gaps in the current literature.
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