Deep-learning approach for caries detection and segmentation on dental bitewing radiographs.
Artificial intelligence
Bitewing radiographs
Deep learning
Dentistry
Tooth caries
Journal
Oral radiology
ISSN: 1613-9674
Titre abrégé: Oral Radiol
Pays: Japan
ID NLM: 8806621
Informations de publication
Date de publication:
10 2022
10 2022
Historique:
received:
22
09
2021
accepted:
09
11
2021
pubmed:
23
11
2021
medline:
17
9
2022
entrez:
22
11
2021
Statut:
ppublish
Résumé
The aim of this study is to recommend an automatic caries detection and segmentation model based on the Convolutional Neural Network (CNN) algorithms in dental bitewing radiographs using VGG-16 and U-Net architecture and evaluate the clinical performance of the model comparing to human observer. A total of 621 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system (CranioCatch, Eskisehir, Turkey) for the detection and segmentation of caries lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Ordu University. VGG-16 and U-Net implemented with PyTorch models were used for the detection and segmentation of caries lesions, respectively. The sensitivity, precision, and F-measure rates for caries detection and caries segmentation were 0.84, 0.81; 0.84, 0.86; and 0.84, 0.84, respectively. Comparing to 5 different experienced observers and AI models on external radiographic dataset, AI models showed superiority to assistant specialists. CNN-based AI algorithms can have the potential to detect and segmentation of dental caries accurately and effectively in bitewing radiographs. AI algorithms based on the deep-learning method have the potential to assist clinicians in routine clinical practice for quickly and reliably detecting the tooth caries. The use of these algorithms in clinical practice can provide to important benefit to physicians as a clinical decision support system in dentistry.
Identifiants
pubmed: 34807344
doi: 10.1007/s11282-021-00577-9
pii: 10.1007/s11282-021-00577-9
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
468-479Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2021. The Author(s) under exclusive licence to Japanese Society for Oral and Maxillofacial Radiology.
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