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

Bilu Xiang; Jiayi Lu; Jiayi Yu

doi:10.1016/j.jdent.2024.105064

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

J Dent. 2024 May 19:146:105064. doi: 10.1016/j.jdent.2024.105064. Online ahead of print.

Authors

Bilu Xiang¹, Jiayi Lu², Jiayi Yu²

Affiliations

¹ School of Dentistry, Shenzhen University Medical School, Shenzhen University, Shenzhen 518000, China. Electronic address: xiangbl@szu.edu.cn.
² Department of Stomatology, Shenzhen University General Hospital, Shenzhen University, Shenzhen 518000, China.

PMID: 38768854
DOI: 10.1016/j.jdent.2024.105064

Abstract

Objectives: 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.

Data: 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).

Sources: 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.

Conclusions: 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.

Clinical significance: 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.

Keywords: Artificial intelligence; CBCT; Convolutional Neural Networks; Meta-analysis; Tooth segmentation.

Publication types

Review