Automatic caries detection in bitewing radiographs: part I-deep learning.
Bitewing
Convolutional neural networks
Dental caries detection
Ensembling
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:
Dec 2023
Dec 2023
Historique:
received:
30
06
2023
accepted:
11
10
2023
pubmed:
16
11
2023
medline:
16
11
2023
entrez:
15
11
2023
Statut:
ppublish
Résumé
The aim of this work was to assemble a large annotated dataset of bitewing radiographs and to use convolutional neural networks to automate the detection of dental caries in bitewing radiographs with human-level performance. A dataset of 3989 bitewing radiographs was created, and 7257 carious lesions were annotated using minimal bounding boxes. The dataset was then divided into 3 parts for the training (70%), validation (15%), and testing (15%) of multiple object detection convolutional neural networks (CNN). The tested CNN architectures included YOLOv5, Faster R-CNN, RetinaNet, and EfficientDet. To further improve the detection performance, model ensembling was used, and nested predictions were removed during post-processing. The models were compared in terms of the [Formula: see text] score and average precision (AP) with various thresholds of the intersection over union (IoU). The twelve tested architectures had [Formula: see text] scores of 0.72-0.76. Their performance was improved by ensembling which increased the [Formula: see text] score to 0.79-0.80. The best-performing ensemble detected caries with the precision of 0.83, recall of 0.77, [Formula: see text], and AP of 0.86 at IoU=0.5. Small carious lesions were predicted with slightly lower accuracy (AP 0.82) than medium or large lesions (AP 0.88). The trained ensemble of object detection CNNs detected caries with satisfactory accuracy and performed at least as well as experienced dentists (see companion paper, Part II). The performance on small lesions was likely limited by inconsistencies in the training dataset. Caries can be automatically detected using convolutional neural networks. However, detecting incipient carious lesions remains challenging.
Identifiants
pubmed: 37968358
doi: 10.1007/s00784-023-05335-1
pii: 10.1007/s00784-023-05335-1
doi:
Types de publication
Journal Article
Langues
eng
Pagination
7463-7471Subventions
Organisme : Ministerstvo Školství, Mládeže a Tělovýchovy
ID : CZ.02.1.01/0.0/0.0/16 019/0000765
Organisme : Všeobecná Fakultní Nemocnice v Praze
ID : GIP-21-SL-01-232
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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