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

Journal: Journal of dentistry

PMID: 38768854

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.

Authors

Bilu Xiang

School of Dentistry, Shenzhen University Medical School, Shenzhen University, Shenzhen 518000, China. Electronic address: xiangbl@szu.edu.cn.
Jiayi Lu

Hong Kong Baptist University, Hong Kong, China.
Jiayi Yu

Department of Stomatology, Shenzhen University General Hospital, Shenzhen University, Shenzhen 518000, China.

Keywords

Algorithms Artificial Intelligence Cone-Beam Computed Tomography Humans Image Processing, Computer-Assisted Imaging, Three-Dimensional Neural Networks, Computer Tooth

External Resources

View on PubMed Access via DOI PubMed (38768854)

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

Abstract

Authors

Keywords

External Resources

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