Automatic caries detection in bitewing radiographs-Part II: experimental comparison.
Bitewing
Convolutional neural networks
Dental caries detection
Ground truth
X-ray images
Journal
Clinical oral investigations
ISSN: 1436-3771
Titre abrégé: Clin Oral Investig
Pays: Germany
ID NLM: 9707115
Informations de publication
Date de publication:
05 Feb 2024
05 Feb 2024
Historique:
received:
19
09
2023
accepted:
23
01
2024
medline:
5
2
2024
pubmed:
5
2
2024
entrez:
5
2
2024
Statut:
epublish
Résumé
The objective of this study was to compare the detection of caries in bitewing radiographs by multiple dentists with an automatic method and to evaluate the detection performance in the absence of a reliable ground truth. Four experts and three novices marked caries using bounding boxes in 100 bitewing radiographs. The same dataset was processed by an automatic object detection deep learning method. All annotators were compared in terms of the number of errors and intersection over union (IoU) using pairwise comparisons, with respect to the consensus standard, and with respect to the annotator of the training dataset of the automatic method. The number of lesions marked by experts in 100 images varied between 241 and 425. Pairwise comparisons showed that the automatic method outperformed all dentists except the original annotator in the mean number of errors, while being among the best in terms of IoU. With respect to a consensus standard, the performance of the automatic method was best in terms of the number of errors and slightly below average in terms of IoU. Compared with the original annotator, the automatic method had the highest IoU and only one expert made fewer errors. The automatic method consistently outperformed novices and performed as well as highly experienced dentists. The consensus in caries detection between experts is low. An automatic method based on deep learning can improve both the accuracy and repeatability of caries detection, providing a useful second opinion even for very experienced dentists.
Identifiants
pubmed: 38315246
doi: 10.1007/s00784-024-05528-2
pii: 10.1007/s00784-024-05528-2
doi:
Types de publication
Journal Article
Langues
eng
Pagination
133Subventions
Organisme : Všeobecná Fakultní Nemocnice v Pranewize
ID : GIP-21-SL-01-232
Organisme : Ministerstvo Školství, Mládeže a Tělovýchovy
ID : CZ.02.1.01/0.0/0.0/16 019/0000765
Informations de copyright
© 2024. The Author(s).
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