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- Publisher Website: 10.1007/s00784-022-04477-y
- Scopus: eid_2-s2.0-85128414274
- PMID: 35438326
- WOS: WOS:000784838500001
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Article: Potential and impact of artificial intelligence algorithms in dento-maxillofacial radiology
Title | Potential and impact of artificial intelligence algorithms in dento-maxillofacial radiology |
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Authors | |
Keywords | Artificial intelligence Convolutional neural network Deep learning Dento-maxillofacial radiology |
Issue Date | 2022 |
Citation | Clinical Oral Investigations, 2022, v. 26, n. 9, p. 5535-5555 How to Cite? |
Abstract | Objectives: Novel artificial intelligence (AI) learning algorithms in dento-maxillofacial radiology (DMFR) are continuously being developed and improved using advanced convolutional neural networks. This review provides an overview of the potential and impact of AI algorithms in DMFR. Materials and methods: A narrative review was conducted on the literature on AI algorithms in DMFR. Results: In the field of DMFR, AI algorithms were mainly proposed for (1) automated detection of dental caries, periapical pathologies, root fracture, periodontal/peri-implant bone loss, and maxillofacial cysts/tumors; (2) classification of mandibular third molars, skeletal malocclusion, and dental implant systems; (3) localization of cephalometric landmarks; and (4) improvement of image quality. Data insufficiency, overfitting, and the lack of interpretability are the main issues in the development and use of image-based AI algorithms. Several strategies have been suggested to address these issues, such as data augmentation, transfer learning, semi-supervised training, few-shot learning, and gradient-weighted class activation mapping. Conclusions: Further integration of relevant AI algorithms into one fully automatic end-to-end intelligent system for possible multi-disciplinary applications is very likely to be a field of increased interest in the future. Clinical relevance: This review provides dental practitioners and researchers with a comprehensive understanding of the current development, performance, issues, and prospects of image-based AI algorithms in DMFR. |
Persistent Identifier | http://hdl.handle.net/10722/329802 |
ISSN | 2023 Impact Factor: 3.1 2023 SCImago Journal Rankings: 0.942 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Hung, Kuo Feng | - |
dc.contributor.author | Ai, Qi Yong H. | - |
dc.contributor.author | Leung, Yiu Yan | - |
dc.contributor.author | Yeung, Andy Wai Kan | - |
dc.date.accessioned | 2023-08-09T03:35:26Z | - |
dc.date.available | 2023-08-09T03:35:26Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Clinical Oral Investigations, 2022, v. 26, n. 9, p. 5535-5555 | - |
dc.identifier.issn | 1432-6981 | - |
dc.identifier.uri | http://hdl.handle.net/10722/329802 | - |
dc.description.abstract | Objectives: Novel artificial intelligence (AI) learning algorithms in dento-maxillofacial radiology (DMFR) are continuously being developed and improved using advanced convolutional neural networks. This review provides an overview of the potential and impact of AI algorithms in DMFR. Materials and methods: A narrative review was conducted on the literature on AI algorithms in DMFR. Results: In the field of DMFR, AI algorithms were mainly proposed for (1) automated detection of dental caries, periapical pathologies, root fracture, periodontal/peri-implant bone loss, and maxillofacial cysts/tumors; (2) classification of mandibular third molars, skeletal malocclusion, and dental implant systems; (3) localization of cephalometric landmarks; and (4) improvement of image quality. Data insufficiency, overfitting, and the lack of interpretability are the main issues in the development and use of image-based AI algorithms. Several strategies have been suggested to address these issues, such as data augmentation, transfer learning, semi-supervised training, few-shot learning, and gradient-weighted class activation mapping. Conclusions: Further integration of relevant AI algorithms into one fully automatic end-to-end intelligent system for possible multi-disciplinary applications is very likely to be a field of increased interest in the future. Clinical relevance: This review provides dental practitioners and researchers with a comprehensive understanding of the current development, performance, issues, and prospects of image-based AI algorithms in DMFR. | - |
dc.language | eng | - |
dc.relation.ispartof | Clinical Oral Investigations | - |
dc.subject | Artificial intelligence | - |
dc.subject | Convolutional neural network | - |
dc.subject | Deep learning | - |
dc.subject | Dento-maxillofacial radiology | - |
dc.title | Potential and impact of artificial intelligence algorithms in dento-maxillofacial radiology | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/s00784-022-04477-y | - |
dc.identifier.pmid | 35438326 | - |
dc.identifier.scopus | eid_2-s2.0-85128414274 | - |
dc.identifier.volume | 26 | - |
dc.identifier.issue | 9 | - |
dc.identifier.spage | 5535 | - |
dc.identifier.epage | 5555 | - |
dc.identifier.eissn | 1436-3771 | - |
dc.identifier.isi | WOS:000784838500001 | - |