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Accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning

Authors
 Joon Im  ;  Ju-Yeong Kim  ;  Hyung-Seog Yu  ;  Kee-Joon Lee  ;  Sung-Hwan Choi  ;  Ji-Hoi Kim  ;  Hee-Kap Ahn  ;  Jung-Yul Cha 
Citation
 SCIENTIFIC REPORTS, Vol.12(1) : 9429, 2022-06 
Journal Title
SCIENTIFIC REPORTS
Issue Date
2022-06
MeSH
Algorithms ; Deep Learning* ; Image Processing, Computer-Assisted / methods ; Models, Dental ; Neural Networks, Computer ; Tooth* / diagnostic imaging
Abstract
This study evaluates the accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning. We developed a dynamic graph convolutional neural network (DGCNN)-based algorithm for automatic tooth segmentation and classification using 516 digital dental models. We segmented 30 digital dental models using three methods for comparison: (1) automatic tooth segmentation (AS) using the DGCNN-based algorithm from LaonSetup software, (2) landmark-based tooth segmentation (LS) using OrthoAnalyzer software, and (3) tooth designation and segmentation (DS) using Autolign software. We evaluated the segmentation success rate, mesiodistal (MD) width, clinical crown height (CCH), and segmentation time. For the AS, LS, and DS, the tooth segmentation success rates were 97.26%, 97.14%, and 87.86%, respectively (p < 0.001, post-hoc; AS, LS > DS), the means of MD widths were 8.51, 8.28, and 8.63 mm, respectively (p < 0.001, post hoc; DS > AS > LS), the means of CCHs were 7.58, 7.65, and 7.52 mm, respectively (p < 0.001, post-hoc; LS > DS, AS), and the means of segmentation times were 57.73, 424.17, and 150.73 s, respectively (p < 0.001, post-hoc; AS < DS < LS). Automatic tooth segmentation of a digital dental model using deep learning showed high segmentation success rate, accuracy, and efficiency; thus, it can be used for orthodontic diagnosis and appliance fabrication.
Files in This Item:
T202205181.pdf Download
DOI
10.1038/s41598-022-13595-2
Appears in Collections:
2. College of Dentistry (치과대학) > Dept. of Orthodontics (교정과학교실) > 1. Journal Papers
Yonsei Authors
Yu, Hyung Seog(유형석) ORCID logo https://orcid.org/0000-0002-1275-5078
Lee, Kee Joon(이기준) ORCID logo https://orcid.org/0000-0002-0782-3128
Cha, Jung Yul(차정열)
Choi, Sung Hwan(최성환) ORCID logo https://orcid.org/0000-0002-1150-0268
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/191592
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