Oral Radiol
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This report aims to summarize the fundamental concepts of Artificial Intelligence (AI), and to provide a non-exhaustive overview of AI applications in dental imaging, comprising diagnostics, forensics, image processing and image reconstruction. AI has arguably become the hottest topic in radiology in recent years owing to the increased computational power available to researchers, the continuing collection of digital data, as well as the development of highly efficient algorithms for machine learning and deep learning. It is now feasible to develop highly robust AI applications that make use of the vast amount of data available to us, and that keep learning and improving over time.
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The aim of this study was to examine the relationship between ramus height, gonial angle and impaction classifications of mandibular third molars. ⋯ Correlation between ramus height/gonial angle and impaction classification types of mandibular third-molar teeth was detected.
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The current coronavirus disease 2019 (COVID-19) outbreak has brought substantial challenges to the world health system, including the practice of dental and maxillofacial radiology (DMFR). DMFR will carry on an imperative role in healthcare during this crisis. This rapid communication has collected and evaluated all the best current evidence and published guidelines as well as professional recommendations to help maxillofacial radiologists and dental practitioners for safer radiological and imaging examinations on healthy, suspected, or confirmed COVID-19 patients during outbreak. Some strategies have been depicted including procedural indications, infection control, and correct employment of personal protection equipment along with evoking the proper practice environment during and after the COVID-19 outbreak.
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To apply a deep-learning system for diagnosis of maxillary sinusitis on panoramic radiography, and to clarify its diagnostic performance. ⋯ The diagnostic performance of the deep-learning system for maxillary sinusitis on panoramic radiographs was sufficiently high. Results from the deep-learning system are expected to provide diagnostic support for inexperienced dentists.