1. The Application of Artificial Intelligence for Tooth Segmentation in CBCT Images: A Systematic Review.
- Author
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Tarce, Mihai, Zhou, You, Antonelli, Alessandro, and Becker, Kathrin
- Subjects
CONE beam computed tomography ,ARTIFICIAL intelligence ,CONVOLUTIONAL neural networks ,DENTURES ,IMAGE processing - Abstract
Objective: To conduct a comprehensive and systematic review of the application of existing artificial intelligence for tooth segmentation in CBCT images. Materials and Methods: A literature search of the MEDLINE, Web of Science, and Scopus databases to find publications from inception through 21 August 2023, non-English publications excluded. The risk of bias and applicability of each article was assessed using QUADAS-2, and data on segmentation category, research model, sample size and groupings, and evaluation metrics were extracted from the articles. Results: A total of 34 articles were included. Artificial intelligence methods mainly involve deep learning-based techniques, including Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), and CNN-based network structures, such as U-Net and V-Net. They utilize multi-stage strategies and combine other mechanisms and algorithms to further improve the semantic or instance segmentation performance of CBCT images, and most of the models have a Dice similarity coefficient greater than 90% and accuracy ranging from 83% to 99%. Conclusions: Artificial intelligence methods have shown excellent performance in tooth segmentation of CBCT images, but still face problems, such as the small size of training data and non-uniformity of evaluation metrics, which still need to be further improved and explored for their application and evaluation in clinical applications. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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