A unique AI-based tool for automated segmentation of pulp cavity structures in maxillary premolars on CBCT

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of Leuven-
Autor(es): dc.contributorUniversidade Federal da Bahia (UFBA)-
Autor(es): dc.contributorJordanian Royal Medical Services-
Autor(es): dc.contributorUniversity Hospitals Leuven-
Autor(es): dc.contributorKarolinska Institute-
Autor(es): dc.creatorSantos-Junior, Airton Oliveira-
Autor(es): dc.creatorFontenele, Rocharles Cavalcante-
Autor(es): dc.creatorNeves, Frederico Sampaio-
Autor(es): dc.creatorAli, Saleem-
Autor(es): dc.creatorJacobs, Reinhilde-
Autor(es): dc.creatorTanomaru-Filho, Mário-
Data de aceite: dc.date.accessioned2025-08-21T17:51:33Z-
Data de disponibilização: dc.date.available2025-08-21T17:51:33Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2025-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1038/s41598-025-86203-8-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/306132-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/306132-
Descrição: dc.descriptionTo develop and validate an artificial intelligence (AI)-driven tool for the automatic segmentation of pulp cavity structures in maxillary premolars teeth on cone-beam computed tomography (CBCT). One hundred and eleven CBCT scans were divided into training (n = 55), validation (n = 14), and testing (n = 42) sets, with manual segmentation serving as the ground truth. The AI tool automatically segmented the testing dataset, with errors corrected by an operator to create refined 3D (R-AI) models. The overall AI performance was assessed by comparing AI and R-AI models, and thirty percent of the test sample was manually segmented to compare AI and human performance. Time-efficiency of each method was recorded in seconds (s). Statistical analysis included independent and paired t-tests to evaluate the effect of tooth type on accuracy metrics and AI versus manual segmentation. One-way ANOVA with Tukey’s post hoc test was used for time efficiency analysis. A 5% significance level was used for all analyses.The AI tool demonstrated excellent performance with Dice similarity coefficients (DSC) ranging from 88% ± 7 to 93% ± 3 and 95% Hausdorff distances (HD) from 0.13 ± 0.06 to 0.16 ± 0.06 mm. Automated segmentation of maxillary second premolars performed slightly better than that of maxillary first premolars in terms of intersection over union (p = 0.005), DSC (p = 0.008), recall (p = 0.008), precision (p = 0.02), and 95% HD (p = 0.04). The AI-based approach showed higher recall (p = 0.04), accuracy (p = 0.01), and lower 95% HD than manual segmentation (p < 0.001). AI segmentation (42.8 ± 8.4 s) was 75 times faster than manual segmentation (3218.7 ± 692.2 s) (p < 0.001). The AI tool proved highly accurate and time-efficient, surpassing human expert performance.-
Descrição: dc.descriptionKarolinska Institutet-
Descrição: dc.descriptionDepartment of Restorative Dentistry School of Dentistry São Paulo State University (UNESP), São Paulo-
Descrição: dc.descriptionOMFS IMPATH Research Group Department of Imaging and Pathology Faculty of Medicine University of Leuven-
Descrição: dc.descriptionDepartment of Propedeutics and Integrated Clinic Division of Oral Radiology School of Dentistry Federal University of Bahia (UFBA), Bahia-
Descrição: dc.descriptionDepartment of Restorative Dentistry King Hussein Medical Center Jordanian Royal Medical Services-
Descrição: dc.descriptionDepartment of Oral and Maxillofacial Surgery University Hospitals Leuven-
Descrição: dc.descriptionDepartment of Dental Medicine Karolinska Institute, Alfred Nobels Allé 8, Stockholm-
Descrição: dc.descriptionDepartment of Restorative Dentistry School of Dentistry São Paulo State University (UNESP), São Paulo-
Idioma: dc.languageen-
Relação: dc.relationScientific Reports-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subject3-D Imaging-
Palavras-chave: dc.subjectArtificial intelligence-
Palavras-chave: dc.subjectCone-beam computed tomography-
Palavras-chave: dc.subjectEndodontics-
Palavras-chave: dc.subjectPremolars-
Título: dc.titleA unique AI-based tool for automated segmentation of pulp cavity structures in maxillary premolars on CBCT-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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