44 81

Cited 0 times in

Deep learning for early dental caries detection in bitewing radiographs

Authors
 Shinae Lee  ;  Sang-Il Oh  ;  Junik Jo  ;  Sumi Kang  ;  Yooseok Shin  ;  Jeong-Won Park 
Citation
 SCIENTIFIC REPORTS, Vol.11(1) : 16807, 2021-08 
Journal Title
SCIENTIFIC REPORTS
Issue Date
2021-08
MeSH
Deep Learning* ; Dental Caries / diagnosis* ; Dental Caries / diagnostic imaging* ; Humans ; Neural Networks, Computer ; Radiography, Bitewing*
Abstract
The early detection of initial dental caries enables preventive treatment, and bitewing radiography is a good diagnostic tool for posterior initial caries. In medical imaging, the utilization of deep learning with convolutional neural networks (CNNs) to process various types of images has been actively researched, with promising performance. In this study, we developed a CNN model using a U-shaped deep CNN (U-Net) for caries detection on bitewing radiographs and investigated whether this model can improve clinicians' performance. The research complied with relevant ethical regulations. In total, 304 bitewing radiographs were used to train the CNN model and 50 radiographs for performance evaluation. The diagnostic performance of the CNN model on the total test dataset was as follows: precision, 63.29%; recall, 65.02%; and F1-score, 64.14%, showing quite accurate performance. When three dentists detected caries using the results of the CNN model as reference data, the overall diagnostic performance of all three clinicians significantly improved, as shown by an increased sensitivity ratio (D1, 85.34%; D1', 92.15%; D2, 85.86%; D2', 93.72%; D3, 69.11%; D3', 79.06%; p < 0.05). These increases were especially significant (p < 0.05) in the initial and moderate caries subgroups. The deep learning model may help clinicians to diagnose dental caries more accurately.
Files in This Item:
T202104811.pdf Download
DOI
10.1038/s41598-021-96368-7
Appears in Collections:
2. College of Dentistry (치과대학) > Dental Education Research Center (치의학교육교실) > 1. Journal Papers
2. College of Dentistry (치과대학) > Dept. of Conservative Dentistry (보존과학교실) > 1. Journal Papers
Yonsei Authors
Kang, Sumi(강수미) ORCID logo https://orcid.org/0000-0002-4682-6540
Park, Jeong Won(박정원) ORCID logo https://orcid.org/0000-0002-2517-8150
Shin, Yoo Seok(신유석) ORCID logo https://orcid.org/0000-0003-1121-2570
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/186909
사서에게 알리기
  feedback

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse

Links