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Artificial Intelligence Application in Assessment of Panoramic Radiographs

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TLDR
The tested AI system can be helpful as an initial evaluation of screening PRs, giving appropriate credibility reports and suggesting additional diagnostic methods for more accurate evaluation if needed.
Abstract
The aim of this study was to assess the reliability of the artificial intelligence (AI) automatic evaluation of panoramic radiographs (PRs). Thirty PRs, covering at least six teeth with the possibility of assessing the marginal and apical periodontium, were uploaded to the Diagnocat (LLC Diagnocat, Moscow, Russia) account, and the radiologic report of each was generated as the basis of automatic evaluation. The same PRs were manually evaluated by three independent evaluators with 12, 15, and 28 years of experience in dentistry, respectively. The data were collected in such a way as to allow statistical analysis with SPSS Statistics software (IBM, Armonk, NY, USA). A total of 90 reports were created for 30 PRs. The AI protocol showed very high specificity (above 0.9) in all assessments compared to ground truth except from periodontal bone loss. Statistical analysis showed a high interclass correlation coefficient (ICC > 0.75) for all interevaluator assessments, proving the good credibility of the ground truth and the reproducibility of the reports. Unacceptable reliability was obtained for caries assessment (ICC = 0.681) and periapical lesions assessment (ICC = 0.619). The tested AI system can be helpful as an initial evaluation of screening PRs, giving appropriate credibility reports and suggesting additional diagnostic methods for more accurate evaluation if needed.

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Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

TL;DR: The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.
Journal ArticleDOI

Deep Learning Based Detection Tool for Impacted Mandibular Third Molar Teeth

Mahmut Celik
- 01 Apr 2022 - 
TL;DR: In this article , a computer-assisted detection system based on deep convolutional neural networks for the detection of third molar impacted teeth using different architectures and to evaluate the potential usefulness and accuracy of the proposed solutions on panoramic radiographs.
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Personalized dental medicine, artificial intelligence and their relevance for dentomaxillofacial imaging.

TL;DR: In this article , a wide range of AI applications, including several commercially available software options, have been proposed to assist dentists in the diagnosis and treatment planning of various dentomaxillofacial diseases, with performance similar or even superior to that of specialists.
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Automated detection of dental restorations using deep learning on panoramic radiographs.

TL;DR: Results showed that the proposed AI-based computer-aided system had great potential with reliable, accurate performance detecting dental restorations, denture and implant in panoramic radiographs.
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Importance of Artificial Intelligence in the analysis of children's CBCT imaging by dental students.

TL;DR: In this paper , the authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported, and that there are no conflicts of interest in their work.
References
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Artificial intelligence in radiology

TL;DR: A general understanding of AI methods, particularly those pertaining to image-based tasks, is established and how these methods could impact multiple facets of radiology is explored, with a general focus on applications in oncology.
Journal Article

Clinical applications of cone-beam computed tomography in dental practice.

TL;DR: An overview of currently available maxillofacial CBCT systems is provided and the specific application of various CBCT display modes to clinical dental practice is reviewed.
Journal ArticleDOI

Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm.

TL;DR: A deep CNN algorithm provided considerably good performance in detecting dental caries in periapical radiographs, and is expected to be among the most effective and efficient methods for diagnosing dental carie.
Journal Article

Accuracy in measurement of distance using limited cone-beam computerized tomography.

TL;DR: LCBCT was shown to be a useful tool for preoperative evaluation in dental surgery because the relatively small field size of its images limits the patient's exposure to radiation.
Journal ArticleDOI

Artificial Intelligence in Dentistry: Chances and Challenges.

TL;DR: This succinct narrative review describes the application, limitations and possible future of AI-based dental diagnostics, treatment planning, and conduct, for example, image analysis, prediction making, record keeping, as well as dental research and discovery.
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Trending Questions (1)
Does AI for caries diagnostics in ragiographs show acceptable ooutcome?

AI for caries diagnostics in panoramic radiographs showed unacceptable reliability (ICC = 0.681) in the study, suggesting limitations in this specific assessment.