Assessment of landmark detection in cephalometric radiographs with different conditions of brightness and contrast using the an artificial intelligence software

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Autor(es): dc.contributorUniversidade Federal da Bahia (UFBA)-
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Federal de Sergipe (UFS)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorSantos Menezes, Liciane dos-
Autor(es): dc.creatorSilva, Thaísa Pinheiro-
Autor(es): dc.creatorLima dos Santos, Marcos Antônio-
Autor(es): dc.creatorHughes, Mariana Mendonça-
Autor(es): dc.creatorReis Mariano Souza, Saulo dos-
Autor(es): dc.creatorLeite Ribeiro, Patrícia Miranda-
Autor(es): dc.creatorLuiz de Freitas, Paulo Henrique-
Autor(es): dc.creatorTakeshita, Wilton Mitsunari-
Data de aceite: dc.date.accessioned2025-08-21T22:22:03Z-
Data de disponibilização: dc.date.available2025-08-21T22:22:03Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1259/dmfr.20230065-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/299400-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/299400-
Descrição: dc.descriptionObjectives To evaluate the reliability and reproducibility of an artificial intelligence (AI) software in identifying cephalometric points on lateral cephalometric radiographs considering four settings of brightness and contrast. Methods and materials Brightness and contrast of 30 lateral cephalometric radiographs were adjusted into four different settings. Then, the control examiner (ECont), the calibrated examiner (ECal), and the CEFBOT AI software (AIs) each marked 19 cephalometric points on all radiographs. Reliability was assessed with a second analysis of the radiographs 15 days after the first one. Statistical significance was set at p < 0.05. Results: Reliability of landmark identification was excellent for the human examiners and the AIs regardless of the type of brightness and contrast setting (mean intraclass correlation coefficient >0.89). When ECont and ECal were compared for reproducibility, there were more cephalometric points with significant differences on the x-axis of the image with the highest contrast and the lowest brightness, namely N(p = 0.033), S(p = 0.030), Po(p < 0.001), and Pog’(p = 0.012). Between ECont and AIs, there were more cephalometric points with significant differences on the image with the highest contrast and the lowest brightness, namely N(p = 0.034), Or(p = 0.048), Po(p < 0.001), A(p = 0.042), Pog’(p = 0.004), Ll(p = 0.005), Ul(p < 0.001), and Sn(p = 0.001). Conclusions While the reliability of the AIs for cephalometric landmark identification was rated as excellent, low brightness and high contrast seemed to affect its reproducibility. The experienced human examiner, on the other hand, did not show such faulty reproducibility; therefore, the AIs used in this study is an excellent auxiliary tool for cephalometric analysis, but still depends on human supervision to be clinically reliable. Dentomaxillofacial Radiology (2023) 52, 20230065. doi: 10.1259/dmfr.20230065 Cite this article as: Menezes LS, Silva TP, Lima dos Santos MA, Hughes MM, Mariano Souza SR, Leite Ribeiro PM, et al. Assessment of landmark detection in cephalometric radiographs with different conditions of brightness and contrast using the an artificial intelligence software.-
Descrição: dc.descriptionDepartment of Oral Diagnosis Federal University of Bahia-
Descrição: dc.descriptionDepartment of Oral Diagnosis Piracicaba Dental School University of Campinas-
Descrição: dc.descriptionDepartment of Oral Diagnosis University of São Paulo-
Descrição: dc.descriptionDepartment of Dentistry Federal University of Sergipe-
Descrição: dc.descriptionDiagnosis and Surgery São Paulo State University (Unesp) School of Dentistry, Araçatuba-
Descrição: dc.descriptionDiagnosis and Surgery São Paulo State University (Unesp) School of Dentistry, Araçatuba-
Idioma: dc.languageen-
Relação: dc.relationDentomaxillofacial Radiology-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectArtificial intelligence-
Palavras-chave: dc.subjectCephalometry-
Palavras-chave: dc.subjectDental radiography-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectRadiology-
Palavras-chave: dc.subjectReproducibility of results-
Título: dc.titleAssessment of landmark detection in cephalometric radiographs with different conditions of brightness and contrast using the an artificial intelligence software-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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