Artificial intelligence as a prediction tool for orthognathic surgery assessment

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of North Carolina at Chapel Hill-
Autor(es): dc.contributorUniversity of Michigan-
Autor(es): dc.contributorPrivate practice-
Autor(es): dc.contributorArthur A. Dugoni School of Dentistry-
Autor(es): dc.creatorde Oliveira, Pedro Henrique José-
Autor(es): dc.creatorLi, Tengfei-
Autor(es): dc.creatorLi, Haoyue-
Autor(es): dc.creatorGonçalves, João Roberto-
Autor(es): dc.creatorSantos-Pinto, Ary-
Autor(es): dc.creatorGandini Junior, Luiz Gonzaga-
Autor(es): dc.creatorCevidanes, Lucia Soares-
Autor(es): dc.creatorToyama, Claudia-
Autor(es): dc.creatorFeltrin, Guilherme Paladini-
Autor(es): dc.creatorCampanha, Antonio Augusto-
Autor(es): dc.creatorde Oliveira Junior, Melchiades Alves-
Autor(es): dc.creatorBianchi, Jonas-
Data de aceite: dc.date.accessioned2025-08-21T15:35:12Z-
Data de disponibilização: dc.date.available2025-08-21T15:35:12Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-10-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1111/ocr.12805-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/305776-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/305776-
Descrição: dc.descriptionIntroduction: An ideal orthodontic treatment involves qualitative and quantitative measurements of dental and skeletal components to evaluate patients' discrepancies, such as facial, occlusal, and functional characteristics. Deciding between orthodontics and orthognathic surgery remains challenging, especially in borderline patients. Advances in technology are aiding clinical decisions in orthodontics. The increasing availability of data and the era of big data enable the use of artificial intelligence to guide clinicians' diagnoses. This study aims to test the capacity of different machine learning (ML) models to predict whether orthognathic surgery or orthodontics treatment is required, using soft and hard tissue cephalometric values. Methods: A total of 920 lateral radiographs from patients previously treated with either conventional orthodontics or in combination with orthognathic surgery were used, comprising n = 558 Class II and n = 362 Class III patients, respectively. Thirty-two measures were obtained from each cephalogram at the initial appointment. The subjects were randomly divided into training (n = 552), validation (n = 183), and test (n = 185) datasets, both as an entire sample and divided into Class II and Class III sub-groups. The extracted data were evaluated using 10 machine learning models and by a four-expert panel consisting of orthodontists (n = 2) and surgeons (n = 2). Results: The combined prediction of 10 models showed top-ranked performance in the testing dataset for accuracy, F1-score, and AUC (entire sample: 0.707, 0.706, 0.791; Class II: 0.759, 0.758, 0.824; Class III: 0.822, 0.807, 0.89). Conclusions: The proposed combined 10 ML approach model accurately predicted the need for orthognathic surgery, showing better performance in Class III patients.-
Descrição: dc.descriptionNational Institute of Dental and Craniofacial Research-
Descrição: dc.descriptionDepartment of Morphology Genetics Orthodontics and Pediatric Dentistry School of Dentistry São Paulo State University (Unesp), São Paulo-
Descrição: dc.descriptionDepartment of Radiology and Biomedical Research Imaging Center University of North Carolina at Chapel Hill-
Descrição: dc.descriptionDepartment of Biostatistics University of North Carolina at Chapel Hill-
Descrição: dc.descriptionDepartment of Orthodontics and Pediatric Dentistry University of Michigan-
Descrição: dc.descriptionPrivate practice, São Paulo-
Descrição: dc.descriptionDepartment of Orthodontics University of the Pacific Arthur A. Dugoni School of Dentistry-
Descrição: dc.descriptionDepartment of Morphology Genetics Orthodontics and Pediatric Dentistry School of Dentistry São Paulo State University (Unesp), São Paulo-
Descrição: dc.descriptionNational Institute of Dental and Craniofacial Research: NIH: R01DE024450-
Formato: dc.format785-794-
Idioma: dc.languageen-
Relação: dc.relationOrthodontics and Craniofacial Research-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectartificial intelligence-
Palavras-chave: dc.subjectorthodontics-
Palavras-chave: dc.subjectorthognathic surgery-
Título: dc.titleArtificial intelligence as a prediction tool for orthognathic surgery assessment-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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