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Article: Radiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion

TitleRadiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion
Authors
Keywordsartificial intelligence
Class III malocclusion
diagnosis and treatment
radiographic imaging
Issue Date4-Mar-2024
PublisherMDPI
Citation
Diagnostics, 2024, v. 14, n. 5 How to Cite?
Abstract

Skeletal Class III malocclusion is one type of dentofacial deformity that significantly affects patients’ facial aesthetics and oral health. The orthodontic treatment of skeletal Class III malocclusion presents challenges due to uncertainties surrounding mandibular growth patterns and treatment outcomes. In recent years, disease-specific radiographic features have garnered interest from researchers in various fields including orthodontics, for their exceptional performance in enhancing diagnostic precision and treatment effect predictability. The aim of this narrative review is to provide an overview of the valuable radiographic features in the diagnosis and management of skeletal Class III malocclusion. Based on the existing literature, a series of analyses on lateral cephalograms have been concluded to identify the significant variables related to facial type classification, growth prediction, and decision-making for tooth extractions and orthognathic surgery in patients with skeletal Class III malocclusion. Furthermore, we summarize the parameters regarding the inter-maxillary relationship, as well as different anatomical structures including the maxilla, mandible, craniofacial base, and soft tissues from conventional and machine learning statistical models. Several distinct radiographic features for Class III malocclusion have also been preliminarily observed using cone beam computed tomography (CBCT) and magnetic resonance imaging (MRI).


Persistent Identifierhttp://hdl.handle.net/10722/344258
ISSN
2023 Impact Factor: 3.0
2023 SCImago Journal Rankings: 0.667

 

DC FieldValueLanguage
dc.contributor.authorLi, Zhuoying-
dc.contributor.authorHung, Kuo Feng-
dc.contributor.authorAi, Qi Yong H-
dc.contributor.authorGu, Min-
dc.contributor.authorSu, Yu-xiong-
dc.contributor.authorShan, Zhiyi-
dc.date.accessioned2024-07-16T03:42:03Z-
dc.date.available2024-07-16T03:42:03Z-
dc.date.issued2024-03-04-
dc.identifier.citationDiagnostics, 2024, v. 14, n. 5-
dc.identifier.issn2075-4418-
dc.identifier.urihttp://hdl.handle.net/10722/344258-
dc.description.abstract<p><span>Skeletal Class III malocclusion is one type of dentofacial deformity that significantly affects patients’ facial aesthetics and oral health. The orthodontic treatment of skeletal Class III malocclusion presents challenges due to uncertainties surrounding mandibular growth patterns and treatment outcomes. In recent years, disease-specific radiographic features have garnered interest from researchers in various fields including orthodontics, for their exceptional performance in enhancing diagnostic precision and treatment effect predictability. The aim of this narrative review is to provide an overview of the valuable radiographic features in the diagnosis and management of skeletal Class III malocclusion. Based on the existing literature, a series of analyses on lateral cephalograms have been concluded to identify the significant variables related to facial type classification, growth prediction, and decision-making for tooth extractions and orthognathic surgery in patients with skeletal Class III malocclusion. Furthermore, we summarize the parameters regarding the inter-maxillary relationship, as well as different anatomical structures including the maxilla, mandible, craniofacial base, and soft tissues from conventional and machine learning statistical models. Several distinct radiographic features for Class III malocclusion have also been preliminarily observed using cone beam computed tomography (CBCT) and magnetic resonance imaging (MRI).</span><br></p>-
dc.languageeng-
dc.publisherMDPI-
dc.relation.ispartofDiagnostics-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectartificial intelligence-
dc.subjectClass III malocclusion-
dc.subjectdiagnosis and treatment-
dc.subjectradiographic imaging-
dc.titleRadiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3390/diagnostics14050544-
dc.identifier.scopuseid_2-s2.0-85187445908-
dc.identifier.volume14-
dc.identifier.issue5-
dc.identifier.eissn2075-4418-
dc.identifier.issnl2075-4418-

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