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Article: The use and performance of artificial intelligence applications in dental and maxillofacial radiology: a systematic review

TitleThe use and performance of artificial intelligence applications in dental and maxillofacial radiology: a systematic review
Authors
KeywordsArtificial intelligence
Computer-assisted
Dentistry
Diagnostic imaging
Radiography
Issue Date2019
PublisherBritish Institute of Radiology. The Journal's web site is located at http://dmfr.birjournals.org/
Citation
Dentomaxillofacial Radiology, 2019, v. 49 n. 1, article no. 20190107 How to Cite?
AbstractOBJECTIVES: To investigate the current clinical applications and diagnostic performance of artificial intelligence (AI) in dental and maxillofacial radiology (DMFR). METHODS: Studies using applications related to DMFR to develop or implement AI models were sought by searching five electronic databases and four selected core journals in the field of DMFR. The customized assessment criteria based on QUADAS-2 were adapted for quality analysis of the studies included. RESULTS: The initial electronic search yielded 1862 titles, and 50 studies were eventually included. Most studies focused on AI applications for an automated localization of cephalometric landmarks, diagnosis of osteoporosis, classification/segmentation of maxillofacial cysts and/or tumors, and identification of periodontitis/periapical disease. The performance of AI models varies among different algorithms. CONCLUSION: The AI models proposed in the studies included exhibited wide clinical applications in DMFR. Nevertheless, it is still necessary to further verify the reliability and applicability of the AI models prior to transferring these models into clinical practice.
Persistent Identifierhttp://hdl.handle.net/10722/279130
ISSN
2017 Impact Factor: 1.848
2015 SCImago Journal Rankings: 0.897
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHung, K-
dc.contributor.authorMontalvao, C-
dc.contributor.authorTanaka, R-
dc.contributor.authorKawai, T-
dc.contributor.authorBornstein, MM-
dc.date.accessioned2019-10-21T02:20:08Z-
dc.date.available2019-10-21T02:20:08Z-
dc.date.issued2019-
dc.identifier.citationDentomaxillofacial Radiology, 2019, v. 49 n. 1, article no. 20190107-
dc.identifier.issn0250-832X-
dc.identifier.urihttp://hdl.handle.net/10722/279130-
dc.description.abstractOBJECTIVES: To investigate the current clinical applications and diagnostic performance of artificial intelligence (AI) in dental and maxillofacial radiology (DMFR). METHODS: Studies using applications related to DMFR to develop or implement AI models were sought by searching five electronic databases and four selected core journals in the field of DMFR. The customized assessment criteria based on QUADAS-2 were adapted for quality analysis of the studies included. RESULTS: The initial electronic search yielded 1862 titles, and 50 studies were eventually included. Most studies focused on AI applications for an automated localization of cephalometric landmarks, diagnosis of osteoporosis, classification/segmentation of maxillofacial cysts and/or tumors, and identification of periodontitis/periapical disease. The performance of AI models varies among different algorithms. CONCLUSION: The AI models proposed in the studies included exhibited wide clinical applications in DMFR. Nevertheless, it is still necessary to further verify the reliability and applicability of the AI models prior to transferring these models into clinical practice.-
dc.languageeng-
dc.publisherBritish Institute of Radiology. The Journal's web site is located at http://dmfr.birjournals.org/-
dc.relation.ispartofDentomaxillofacial Radiology-
dc.subjectArtificial intelligence-
dc.subjectComputer-assisted-
dc.subjectDentistry-
dc.subjectDiagnostic imaging-
dc.subjectRadiography-
dc.titleThe use and performance of artificial intelligence applications in dental and maxillofacial radiology: a systematic review-
dc.typeArticle-
dc.identifier.emailMontalvao, C: montlv@hku.hk-
dc.identifier.emailTanaka, R: rayt3@hku.hk-
dc.identifier.emailBornstein, MM: bornst@hku.hk-
dc.identifier.authorityTanaka, R=rp02130-
dc.identifier.authorityBornstein, MM=rp02217-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1259/dmfr.20190107-
dc.identifier.pmid31386555-
dc.identifier.scopuseid_2-s2.0-85076448724-
dc.identifier.hkuros307390-
dc.identifier.volume49-
dc.identifier.issue1-
dc.identifier.spagearticle no. 20190107-
dc.identifier.epagearticle no. 20190107-
dc.identifier.isiWOS:000502611400001-
dc.publisher.placeUnited Kingdom-

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