Tarce, Mihai and Zhou, You and Antonelli, Alessandro and Becker, Kathrin (2024) The Application of Artificial Intelligence for Tooth Segmentation in CBCT Images: A Systematic Review. Applied Sciences, 14 (14). p. 6298. ISSN 2076-3417
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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.
Item Type: | Article |
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Subjects: | European Repository > Multidisciplinary |
Depositing User: | Managing Editor |
Date Deposited: | 20 Jul 2024 10:08 |
Last Modified: | 20 Jul 2024 10:08 |
URI: | http://go7publish.com/id/eprint/4532 |