To test local grey-scale changes on dental bitewing radiographs near filling margins for image acquisition. Forty approximal preparations in caries-free amalgam filled teeth and bitewing radiographs were acquired under standardized conditions applying four techniques. Film-based analog radiographs were digitized using flat-bed scanner (FDR). Phosphor-plate computed radiographs (PCR) were directly acquired by scanning VistaScan imaging plates. Image quality was tested using Preset Filter (PF) or manually applied IntraOral Fine Filter (IF) to enhance digital images. Local changes from digital imaging processing were assessed by comparing the margin-near (MN) and margin-far (MF) zone by a multivariate repeated measurements analysis. All images were acquired with 8-bit depth (256 shades). Dentine was displayed in 79 shades for FDR and 54 shades for PCR. PF or IF locally modify bitewing radiographs by darkening marginal dentine by 8 or 29 shades, respectively. The sharpest display of the margin (shades per pixel) from dentine to filling was found for IF (26.2), followed by FDR (23.2), PF (15.3) and PCR (8.3). Computed radiography with phosphor plates generate more homogeneous images compared to flatbed-digitized film-based radiographs. The filling margin was sharpest represented with the IF filter at the detriment of an artificial darkening of the dentine near the margin of the filling. Contour artifacts by filters have the potential to confound diagnosis of secondary caries. Algorithms and filters for sensor data processing, causing local changes above 2% of the dynamic range by non-continuous mathematical functions, should only be applied with caution, manually when diagnosing and reversibly.
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http://dx.doi.org/10.1016/j.ejrad.2023.111004 | 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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