Influence of dental fillings and tooth type on the performance of a novel artificial intelligence-driven tool for automatic tooth segmentation on CBCT images – A validation study

Autor: Rocharles Cavalcante Fontenele, Maurício do Nascimento Gerhardt, Jáder Camilo Pinto, Adriaan Van Gerven, Holger Willems, Reinhilde Jacobs, Deborah Queiroz Freitas
Přispěvatelé: Faculty of Medicine, Universidade Estadual de Campinas (UNICAMP), Pontifical Catholic University of Rio Grande do Sul, Universidade Estadual Paulista (UNESP), Innovatie-en incubatiecentrum KU Leuven, University Hospitals Leuven, Karolinska Institute
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Scopus
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
Popis: Made available in DSpace on 2022-04-28T19:51:46Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-04-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Objectives: To assess the influence of dental fillings on the performance of an artificial intelligence (AI)-driven tool for tooth segmentation on cone-beam computed tomography (CBCT) according to the type of tooth. Methods: A total of 175 CBCT scans (500 teeth) were recruited for performing training (140 CBCT scans - 400 teeth) and validation (35 CBCT scans - 100 teeth) of the AI convolutional neural networks. The test dataset involved 74 CBCT scans (226 teeth), which was further divided into control and experimental groups depending on the presence of dental filling: without filling (control group: 24 CBCT scans – 113 teeth) and with coronal and/or root filling (experimental group: 50 CBCT scans – 113 teeth). The segmentation performance for both groups was assessed. Additionally, 10% of each tooth type (anterior, premolar, and molar) was randomly selected for time analysis according to manual, AI-based and refined-AI segmentation methods. Results: The presence of fillings significantly influenced the segmentation performance (p
Databáze: OpenAIRE
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