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Article: Current Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice

TitleCurrent Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice
Authors
Keywordsartificial intelligence
AI
machine learning
ML
cone beam computed tomography (CBCT)
Issue Date2020
PublisherMolecular Diversity Preservation International. The Journal's web site is located at http://www.mdpi.org/ijerph
Citation
International Journal of Environmental Research and Public Health, 2020, v. 17 n. 12, p. article no. 4424 How to Cite?
AbstractThe increasing use of three-dimensional (3D) imaging techniques in dental medicine has boosted the development and use of artificial intelligence (AI) systems for various clinical problems. Cone beam computed tomography (CBCT) and intraoral/facial scans are potential sources of image data to develop 3D image-based AI systems for automated diagnosis, treatment planning, and prediction of treatment outcome. This review focuses on current developments and performance of AI for 3D imaging in dentomaxillofacial radiology (DMFR) as well as intraoral and facial scanning. In DMFR, machine learning-based algorithms proposed in the literature focus on three main applications, including automated diagnosis of dental and maxillofacial diseases, localization of anatomical landmarks for orthodontic and orthognathic treatment planning, and general improvement of image quality. Automatic recognition of teeth and diagnosis of facial deformations using AI systems based on intraoral and facial scanning will very likely be a field of increased interest in the future. The review is aimed at providing dental practitioners and interested colleagues in healthcare with a comprehensive understanding of the current trend of AI developments in the field of 3D imaging in dental medicine.
Persistent Identifierhttp://hdl.handle.net/10722/283773
ISSN
2019 Impact Factor: 2.849
2015 SCImago Journal Rankings: 0.883
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHUNG, KF-
dc.contributor.authorYeung, AWK-
dc.contributor.authorTanaka, R-
dc.contributor.authorBornstein, MM-
dc.date.accessioned2020-07-03T08:23:54Z-
dc.date.available2020-07-03T08:23:54Z-
dc.date.issued2020-
dc.identifier.citationInternational Journal of Environmental Research and Public Health, 2020, v. 17 n. 12, p. article no. 4424-
dc.identifier.issn1661-7827-
dc.identifier.urihttp://hdl.handle.net/10722/283773-
dc.description.abstractThe increasing use of three-dimensional (3D) imaging techniques in dental medicine has boosted the development and use of artificial intelligence (AI) systems for various clinical problems. Cone beam computed tomography (CBCT) and intraoral/facial scans are potential sources of image data to develop 3D image-based AI systems for automated diagnosis, treatment planning, and prediction of treatment outcome. This review focuses on current developments and performance of AI for 3D imaging in dentomaxillofacial radiology (DMFR) as well as intraoral and facial scanning. In DMFR, machine learning-based algorithms proposed in the literature focus on three main applications, including automated diagnosis of dental and maxillofacial diseases, localization of anatomical landmarks for orthodontic and orthognathic treatment planning, and general improvement of image quality. Automatic recognition of teeth and diagnosis of facial deformations using AI systems based on intraoral and facial scanning will very likely be a field of increased interest in the future. The review is aimed at providing dental practitioners and interested colleagues in healthcare with a comprehensive understanding of the current trend of AI developments in the field of 3D imaging in dental medicine.-
dc.languageeng-
dc.publisherMolecular Diversity Preservation International. The Journal's web site is located at http://www.mdpi.org/ijerph-
dc.relation.ispartofInternational Journal of Environmental Research and Public Health-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectartificial intelligence-
dc.subjectAI-
dc.subjectmachine learning-
dc.subjectML-
dc.subjectcone beam computed tomography (CBCT)-
dc.titleCurrent Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice-
dc.typeArticle-
dc.identifier.emailYeung, AWK: ndyeung@hku.hk-
dc.identifier.emailTanaka, R: rayt3@hku.hk-
dc.identifier.authorityYeung, AWK=rp02143-
dc.identifier.authorityTanaka, R=rp02130-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3390/ijerph17124424-
dc.identifier.pmid32575560-
dc.identifier.pmcidPMC7345758-
dc.identifier.scopuseid_2-s2.0-85086761816-
dc.identifier.hkuros310674-
dc.identifier.volume17-
dc.identifier.issue12-
dc.identifier.spagearticle no. 4424-
dc.identifier.epagearticle no. 4424-
dc.identifier.isiWOS:000554751800001-
dc.publisher.placeSwitzerland-
dc.identifier.issnl1660-4601-

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