Oral screening of dental calculus, gingivitis and dental caries through segmentation on intraoral photographic images using deep learning.
Deep learning
Dental image segmentation
Intraoral camera
State space models
Journal
BMC oral health
ISSN: 1472-6831
Titre abrégé: BMC Oral Health
Pays: England
ID NLM: 101088684
Informations de publication
Date de publication:
25 Oct 2024
25 Oct 2024
Historique:
received:
16
04
2024
accepted:
15
10
2024
medline:
26
10
2024
pubmed:
26
10
2024
entrez:
25
10
2024
Statut:
epublish
Résumé
Intraoral photographic images are instrumental in the early screening and clinical diagnosis of oral diseases. In addition, people have been trying to apply artificial intelligence to these images. The purpose of this study is to investigate and evaluate a deep learning system designed to segment intraoral photographic images for the detection of dental caries, dental calculus, and gingivitis, and to assess the degree of dental calculus based on the overall features of the tooth surface and gingival margin. This cross-sectional study collected 3,365 oral endoscopic images, randomly distributed in training datasets (2,019 images), validation dataset (673 images), and test dataset (673 images). The training set and verification set images are manually labeled. An oral endoscopic image segmentation method based on Mamba (Oral-Mamba) and an intelligent evaluation model of dental calculus degree were proposed, achieving the segmentation of two types of oral diseases, namely gingivitis and dental caries, as well as the segmentation of dental calculus regions, and the intelligent evaluation of the degree of dental calculus. Oral-Mamba demonstrated high accuracy in segmentation, with accuracy rates for gingivitis, dental caries, and dental calculus at 0.83, 0.83, and 0.81, respectively. In particular, these rates surpassed those of the U-Net model in IoU, accuracy, and recall metrics. Furthermore, Oral-Mamba runs 25% faster than U-Net.The accuracy of degree classification in the intelligent evaluation model of dental calculus degree is 85%. The proposed deep learning system is expected to be used for the detection of two types of oral diseases and dental calculus, and the degree judgment of photographic images from an intraoral camera. This system offers a practical method to assist in the oral screening of dental caries, dental calculus, and gingivitis, providing benefits such as intuitive use, time efficiency, cost-effectiveness, and ease of deployment.
Identifiants
pubmed: 39455942
doi: 10.1186/s12903-024-05072-1
pii: 10.1186/s12903-024-05072-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1287Subventions
Organisme : This research was supported by the Startup Foundation for Introducing Talent of NUIST (No. 2023r124) and Enterprise Cooperation Project (No. 2023h852)
ID : (No. 2023r124) and (No. 2023h852)
Organisme : This research was supported by the Startup Foundation for Introducing Talent of NUIST (No. 2023r124) and Enterprise Cooperation Project (No. 2023h852)
ID : (No. 2023r124) and (No. 2023h852)
Informations de copyright
© 2024. The Author(s).
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