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The efficiency of a...
The efficiency of artificial intelligence methods for finding radiographic features in different endodontic treatments-a systematic review
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Ramezanzade, S. (författare)
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Laurentiu, T. (författare)
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Bakhshandah, A. (författare)
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visa fler...
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Ibragimov, B. (författare)
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- Kvist, Thomas, 1959 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för odontologi,Institute of Odontology
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Bjorndal, L. (författare)
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visa färre...
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(creator_code:org_t)
- 2023
- 2023
- Engelska.
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Ingår i: Acta Odontologica Scandinavica. - 0001-6357. ; 81:6, s. 422-435
- Relaterad länk:
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https://gup.ub.gu.se...
Abstract
Ämnesord
Stäng
- ObjectivesTo assess the efficiency of AI methods in finding radiographic features in Endodontic treatment considerations.Material and methodsThis review was based on the PRISMA guidelines and QUADAS 2 tool. A systematic search was performed of the literature on cases with endodontic treatments, comparing AI algorithms (test) versus conventional image assessments (control) for finding radiographic features . The search was conducted in PubMed, Scopus, Google Scholar and the Cochrane library. Inclusion criteria were studies on the use of AI and machine learning in endodontic treatments using dental X-rays.ResultsThe initial search retrieved 1131 papers, from which 24 were included. High heterogeneity of the materials left out a meta-analysis.The reported subcategories were periapical lesion, vertical root fractures, predicting root/canal morphology, locating minor apical foramen, tooth segmentation and endodontic retreatment prediction. Radiographic features assessed were mostly periapical lesions. The studies mostly considered the decision of 1-3 experts as the reference for training their models. Almost half of the included materials campared their trained neural network model with other methods. More than 58% of studies had some level of bias.ConclusionsAI-based models have shown effectiveness in finding radiographic features in different endodontic treatments. While the reported accuracy measurements seem promising, the papers mostly were biased methodologically.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Odontologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Dentistry (hsv//eng)
Nyckelord
- Artificial intelligence
- deep learning
- endodontics
- endodontic
- diagnosis
- machine learning
- minor apical foramen
- neural-network
- periapical lesions
- segmentation
- diagnosis
- Dentistry
- Oral Surgery & Medicine
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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