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Automatic detection of mesiodens on panoramic radiographs using artificial intelligence

Authors
 Eun-Gyu Ha  ;  Kug Jin Jeon  ;  Young Hyun Kim  ;  Jae-Young Kim  ;  Sang-Sun Han 
Citation
 SCIENTIFIC REPORTS, Vol.11(1) : 23061, 2021-11 
Journal Title
SCIENTIFIC REPORTS
Issue Date
2021-11
Abstract
This study aimed to develop an artificial intelligence model that can detect mesiodens on panoramic radiographs of various dentition groups. Panoramic radiographs of 612 patients were used for training. A convolutional neural network (CNN) model based on YOLOv3 for detecting mesiodens was developed. The model performance according to three dentition groups (primary, mixed, and permanent dentition) was evaluated, both internally (130 images) and externally (118 images), using a multi-center dataset. To investigate the effect of image preprocessing, contrast-limited histogram equalization (CLAHE) was applied to the original images. The accuracy of the internal test dataset was 96.2% and that of the external test dataset was 89.8% in the original images. For the primary, mixed, and permanent dentition, the accuracy of the internal test dataset was 96.7%, 97.5%, and 93.3%, respectively, and the accuracy of the external test dataset was 86.7%, 95.3%, and 86.7%, respectively. The CLAHE images yielded less accurate results than the original images in both test datasets. The proposed model showed good performance in the internal and external test datasets and had the potential for clinical use to detect mesiodens on panoramic radiographs of all dentition types. The CLAHE preprocessing had a negligible effect on model performance.
Files in This Item:
T202105560.pdf Download
DOI
10.1038/s41598-021-02571-x
Appears in Collections:
2. College of Dentistry (치과대학) > Dept. of Oral and Maxillofacial Radiology (영상치의학교실) > 1. Journal Papers
2. College of Dentistry (치과대학) > Dept. of Oral and Maxillofacial Surgery (구강악안면외과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Jae Young(김재영) ORCID logo https://orcid.org/0000-0002-9423-438X
Jeon, Kug Jin(전국진) ORCID logo https://orcid.org/0000-0002-5862-2975
Han, Sang Sun(한상선) ORCID logo https://orcid.org/0000-0003-1775-7862
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/187317
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