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(a) principal: Fontenele, Rocharles Cavalcante
• Data de Publicação: 2022
• Outros Autores: Gerhardt, Maurício do Nascimento, Pinto, Jáder Camilo [UNESP], Van Gerven, Adriaan, Willems, Holger, Jacobs, Reinhilde, Freitas, Deborah Queiroz
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.jdent.2022.104069; http://hdl.handle.net/11449/223607
• topic: Artificial intelligence; Cone-beam computed tomography; Convolutional neural network; Fillings; Tooth

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<0.05). However, the accuracy metrics showed an excellent range of values for both control (95% Hausdorff Distance (95% HD): 0.01–0.08 mm; Intersection over union (IoU): 0.97–0.99; Dice similarity coefficient (DSC): 0.98–0.99; Precision: 1.00; Recall: 0.97–0.99; Accuracy: 1.00) and experimental groups (95% HD: 0.17–0.25 mm; IoU: 0.91–0.95; DSC: 0.95–0.97; Precision:1.00; Recall: 0.91–0.95; Accuracy: 0.99–1.00). The time analysis showed that the AI-based segmentation was significantly faster with a mean time of 29.8 s (p<0.001). Conclusions: The proposed AI-driven tool allowed an accurate and time-efficient approach for the segmentation of teeth on CBCT images irrespective of the presence of high-density dental filling material and the type of tooth. Clinical significance: Tooth segmentation is a challenging and time-consuming task, mainly in the presence of artifacts generated by dental filling material. The proposed AI-driven tool could offer a clinically acceptable approach for tooth segmentation, to be applied in the digital dental workflows considering its time efficiency and high accuracy regardless of the presence of dental fillings.