Assessment of YOLOv3 for caries detection in bitewing radiographs based on the ICCMS™ radiographic scoring system.
Bitewing radiograph
Caries detection
Deep learning
Intersection over union
YOLOv3 model
Journal
Clinical oral investigations
ISSN: 1436-3771
Titre abrégé: Clin Oral Investig
Pays: Germany
ID NLM: 9707115
Informations de publication
Date de publication:
Apr 2023
Apr 2023
Historique:
received:
12
07
2022
accepted:
18
11
2022
medline:
17
4
2023
pubmed:
29
11
2022
entrez:
28
11
2022
Statut:
ppublish
Résumé
To assess the feasibility of the YOLOv3 model under the intersection over union (IoU) thresholds of 0.5 (IoU We trained the YOLOv3 model by feeding 994 annotated radiographs with the IoU Regarding the 4-class classification representing caries severity, YOLOv3 could accurately detect and classify enamel caries and initial dentin caries (class RA) (IoU YOLOv3 yielded acceptable performances in both IoU YOLOv3 could be implemented to detect and classify dental caries according to the ICCMS™ classification with acceptable performances to assist dentists in making treatment decisions.
Identifiants
pubmed: 36441268
doi: 10.1007/s00784-022-04801-6
pii: 10.1007/s00784-022-04801-6
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1731-1742Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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