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Assessment of pre-trained deep learning models in the detection of metal artifacts in axial cone beam tomography slices.

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Bibliographic Details
Title: Assessment of pre-trained deep learning models in the detection of metal artifacts in axial cone beam tomography slices.
Authors: Shetty S; Department of Oral and Craniofacial Health Sciences, College of Dental medicine, University of Sharjah, Sharjah, United Arab Emirates., Talaat W; Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates., Al-Rawi N; Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates., AlKawas S; Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates., Sadek M; Department of Orthodontics, Pediatric and Community Dentistry, College of Dental medicine, University of Sharjah, Sharjah, United Arab Emirates., Rego R; Department of Oral and Craniofacial Health Sciences, College of Dental medicine, University of Sharjah, Sharjah, United Arab Emirates., Gaballah K; Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates., Narasimhan S; Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, United Arab Emirates., Alsaegh MA; Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, United Arab Emirates., Hamdoon Z; Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, United Arab Emirates.
Source: Dento maxillo facial radiology [Dentomaxillofac Radiol] 2026 May 26. Date of Electronic Publication: 2026 May 26.
Publication Type: Journal Article
Journal Info: Publisher: Oxford University Press Country of Publication: England NLM ID: 7609576 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-542X (Electronic) Linking ISSN: 0250832X NLM ISO Abbreviation: Dentomaxillofac Radiol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1476-542X
DOI:10.1093/dmfr/twag037