Evaluating tooth segmentation accuracy and time efficiency in CBCT images using artificial intelligence: A systematic review and Meta-analysis

• Published in: Journal of dentistry Vol. 146; p. 105064
• Main Authors: Xiang, Bilu, Lu, Jiayi, Yu, Jiayi
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.07.2024
• Subjects: Algorithms; Artificial Intelligence; CBCT; Cone-Beam Computed Tomography - methods; Convolutional Neural Networks; Humans; Image Processing, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Meta-analysis; Neural Networks, Computer; Tooth - diagnostic imaging; Tooth segmentation; Convolutional Neural Networks; Tooth segmentation; Artificial intelligence; CBCT; Meta-analysis
• ISSN: 0300-5712; 1879-176X; 1879-176X
• DOI: 10.1016/j.jdent.2024.105064

•AI-based models achieve high accuracy (DSC=0.95, 95 %CI 0.94 to 0.96) in delineating tooth boundaries from CBCT images, reducing reliance on manual methods.•AI algorithms significantly reduce tooth segmentation time, enabling faster and more efficient clinical applications (around 10 times faster than manual segmentation).•Convolutional Neural Networks, particularly the U-net model, were found to be the most commonly utilized AI-based approaches for tooth segmentation. This systematic review and meta-analysis aimed to assess the current performance of artificial intelligence (AI)-based methods for tooth segmentation in three-dimensional cone-beam computed tomography (CBCT) images, with a focus on their accuracy and efficiency compared to those of manual segmentation techniques. The data analyzed in this review consisted of a wide range of research studies utilizing AI algorithms for tooth segmentation in CBCT images. Meta-analysis was performed, focusing on the evaluation of the segmentation results using the dice similarity coefficient (DSC). PubMed, Embase, Scopus, Web of Science, and IEEE Explore were comprehensively searched to identify relevant studies. The initial search yielded 5642 entries, and subsequent screening and selection processes led to the inclusion of 35 studies in the systematic review. Among the various segmentation methods employed, convolutional neural networks, particularly the U-net model, are the most commonly utilized. The pooled effect of the DSC score for tooth segmentation was 0.95 (95 %CI 0.94 to 0.96). Furthermore, seven papers provided insights into the time required for segmentation, which ranged from 1.5 s to 3.4 min when utilizing AI techniques. AI models demonstrated favorable accuracy in automatically segmenting teeth from CBCT images while reducing the time required for the process. Nevertheless, correction methods for metal artifacts and tooth structure segmentation using different imaging modalities should be addressed in future studies. AI algorithms have great potential for precise tooth measurements, orthodontic treatment planning, dental implant placement, and other dental procedures that require accurate tooth delineation. These advances have contributed to improved clinical outcomes and patient care in dental practice.