The Application of Artificial Intelligence for Tooth Segmentation in CBCT Images: A Systematic Review
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Dental Radiography and Imaging · Radiomics and Machine Learning in Medical Imaging · AI in cancer detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Periodontology and Implant Dentistry Division, Faculty of Dentistry, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
School of Dentistry, Health Sciences Department, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
Department of Orthodontics and Dentofacial Orthopaedics, Charité-Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany
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