A Dual Discriminator Adversarial Learning Approach for Dental Occlusal Surface Reconstruction.
Journal
Journal of healthcare engineering
ISSN: 2040-2309
Titre abrégé: J Healthc Eng
Pays: England
ID NLM: 101528166
Informations de publication
Date de publication:
2022
2022
Historique:
received:
22
09
2021
accepted:
12
03
2022
entrez:
22
4
2022
pubmed:
23
4
2022
medline:
26
4
2022
Statut:
epublish
Résumé
Restoring the correct masticatory function of partially edentulous patient is a challenging task primarily due to the complex tooth morphology between individuals. Although some deep learning-based approaches have been proposed for dental restorations, most of them do not consider the influence of dental biological characteristics for the occlusal surface reconstruction. Experiments on 1000 real-world patient dental samples demonstrate the effectiveness of our method. For quantitative comparison, the image quality metrics are used to measure the similarity of the generated occlusal surface, and the root mean square between the generated result and the target crown calculated by our method is 0.114 mm. In qualitative analysis, the proposed approach can generate more reasonable dental biological morphology. The results demonstrate that our method significantly outperforms the state-of-the-art methods in occlusal surface reconstruction. Importantly, the designed occlusal surface has enough anatomical morphology of natural teeth and superior clinical application value.
Identifiants
pubmed: 35449834
doi: 10.1155/2022/1933617
pmc: PMC9018184
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1933617Informations de copyright
Copyright © 2022 Sukun Tian et al.
Déclaration de conflit d'intérêts
The authors declare no conflicts of interest.
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