Deep learning based approach: automated gingival inflammation grading model using gingival removal strategy.
Auxiliary diagnosis
Deep learning
Gingival inflammation
Intra-oral photo image
Periodontal disease
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
26 08 2024
26 08 2024
Historique:
received:
11
06
2024
accepted:
14
08
2024
medline:
27
8
2024
pubmed:
27
8
2024
entrez:
26
8
2024
Statut:
epublish
Résumé
Gingival inflammation grade serves as a well-established index in periodontitis. The aim of this study was to develop a deep learning network utilizing a novel feature extraction method for the automatic assessment of gingival inflammation. T-distributed Stochastic Neighbor Embedding (t-SNE) was utilized for dimensionality reduction. A convolutional neural network (CNN) model based on DenseNet was developed for the identification and evaluation of gingival inflammation. To enhance the performance of the deep learning (DL) model, a novel teeth removal algorithm was implemented. Additionally, a Grad-CAM + + encoder was applied to generate heatmaps for computer visual attention analysis. The mean Intersection over Union (MIoU) for the identification of gingivitis was 0.727 ± 0.117. The accuracy rates for the five inflammatory degrees were 77.09%, 77.25%, 74.38%, 73.68% and 79.22%. The Area Under the Receiver Operating Characteristic (AUROC) values were 0.83, 0.80, 0.81, 0.81 and 0.84, respectively. The attention ratio towards gingival tissue increased from 37.73% to 62.20%, and within 8 mm of the gingival margin, it rose from 21.11% to 38.23%. On the gingiva, the overall attention ratio increased from 51.82% to 78.21%. The proposed DL model with novel feature extraction method provides high accuracy and sensitivity for identifying and grading gingival inflammation.
Identifiants
pubmed: 39187553
doi: 10.1038/s41598-024-70311-y
pii: 10.1038/s41598-024-70311-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
19780Subventions
Organisme : Natural Science Foundation of Hubei Province of China
ID : 2021CFB466
Organisme : Medical Backbone Talents Foundation of Wuhan City of China
ID : 2020-55
Informations de copyright
© 2024. The Author(s).
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