Tooth Development Prediction Using a Generative Machine Learning Approach

Kokomoto, Kazuma; Okawa, Rena; Nakano, Kazuhiko; Nozaki, Kazunori

doi:https://doi.org/10.1109/ACCESS.2024.3416748

Tooth Development Prediction Using a Generative Machine Learning Approach

Kokomoto, Kazuma; Okawa, Rena; Nakano, Kazuhiko et al.

IEEE Access, 2024, 12, 87645-87652

Number of Access:781（2025-07-20 12:05 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/97272

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Title	Tooth Development Prediction Using a Generative Machine Learning Approach
Creator	NRID 1000000803107 1000000803107 ORCID 0000-0003-4362-5433 0000-0003-4362-5433 researchmap permalink kokomoto kokomoto Scopus Author ID 57193763258 57193763258 CreatorName Kokomoto, Kazuma
	NRID 1000080437384 1000080437384 researchmap permalink read0091517 read0091517 Scopus Author ID 9744705500 9744705500 ORCID 0000-0002-8634-209X 0000-0002-8634-209X CreatorName Okawa, Rena
	NRID 1000000379083 1000000379083 researchmap permalink read0202427 read0202427 Scopus Author ID 7402011667 7402011667 CreatorName Nakano, Kazuhiko
	NRID 1000040379110 1000040379110 ORCID 0000-0003-3378-0915 0000-0003-3378-0915 researchmap permalink dentgrid dentgrid Scopus Author ID 36665004400 36665004400 CreatorName Nozaki, Kazunori
Subject	Artificial intelligence
	dental informatics
	machine learning
	medical informatics applications
	orthodontics
	pediatric dentistry
Description	Kokomoto K., Okawa R., Nakano K., et al. Tooth Development Prediction Using a Generative Machine Learning Approach. IEEE Access 12, 87645 (2024); https://doi.org/10.1109/ACCESS.2024.3416748.
Abstract	This study pioneers the use of generative deep learning in pediatric dentistry to predict dental growth using panoramic radiography, going beyond numerical analysis and providing dynamic representations of tooth development. We employed StyleGAN-XL, a state-of-the-art generative adversarial network (GAN), to generate realistic images of dental development stages in children. Our dataset consisted of 8,092 anonymized panoramic radiographs from Osaka University Dental Hospital containing various dentition stages and conditions. By interpolating latent vectors from primary or mixed dentition images with those from permanent dentition, we generated continuous transitioning images that visually represented the progression of dental development. The performance of the StyleGAN-XL model was evaluated using Fréchet inception distance scores. Pivotal tuning inversion was used to project real images onto the model's latent space, allowing us to effectively interpolate between current and future dental states. The resulting images showed a smooth transition from primary to permanent dentition, closely resembling the actual stages of dental development. This method represents a significant advancement in dental imaging and predictive analytics, offering a novel approach for clinicians and patients to visualize and understand dental growth. Our findings suggest broader applications for generative models in medical imaging, extending beyond traditional enhancement and modeling tasks. Our study highlights the transformative potential of GANs in medical imaging and provides a foundation for future advancements in predictive dentistry.
Publisher	Institute of Electrical and Electronics Engineers Inc.
Source Title	IEEE Access
Volume (Issue)	12
Page	87645-87652
Date of Issued	2024-06-28
Language	English
Handle URL	https://hdl.handle.net/11094/97272
eISSN	21693536
Relation.isIdenticalTo	DOI (Publisher Version) https://doi.org/10.1109/ACCESS.2024.3416748
Access Rights	open access
CopyRight	This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
oaire:version	Version of Record
Category	学術雑誌論文 Journal Article

Resource Type	journal article
Local Type	Journal Article
DCTERMS.bibliographicCitation	IEEE Access. 2024, 12, p. 87645-87652
DC.title	Tooth Development Prediction Using a Generative Machine Learning Approach
DC.creator	Kokomoto, Kazuma
	Okawa, Rena
	Nakano, Kazuhiko
	Nozaki, Kazunori
DC.publisher	Institute of Electrical and Electronics Engineers Inc.
DC.language' scheme='DCTERMS.RFC1766	English
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2024-06-28
DC.identifier	https://doi.org/10.1109/ACCESS.2024.3416748
DC.subject	Artificial intelligence
	dental informatics
	machine learning
	medical informatics applications
	orthodontics
	pediatric dentistry
DC.description	Kokomoto K., Okawa R., Nakano K., et al. Tooth Development Prediction Using a Generative Machine Learning Approach. IEEE Access 12, 87645 (2024); https://doi.org/10.1109/ACCESS.2024.3416748.
DCTERMS.abstract	This study pioneers the use of generative deep learning in pediatric dentistry to predict dental growth using panoramic radiography, going beyond numerical analysis and providing dynamic representations of tooth development. We employed StyleGAN-XL, a state-of-the-art generative adversarial network (GAN), to generate realistic images of dental development stages in children. Our dataset consisted of 8,092 anonymized panoramic radiographs from Osaka University Dental Hospital containing various dentition stages and conditions. By interpolating latent vectors from primary or mixed dentition images with those from permanent dentition, we generated continuous transitioning images that visually represented the progression of dental development. The performance of the StyleGAN-XL model was evaluated using Fréchet inception distance scores. Pivotal tuning inversion was used to project real images onto the model's latent space, allowing us to effectively interpolate between current and future dental states. The resulting images showed a smooth transition from primary to permanent dentition, closely resembling the actual stages of dental development. This method represents a significant advancement in dental imaging and predictive analytics, offering a novel approach for clinicians and patients to visualize and understand dental growth. Our findings suggest broader applications for generative models in medical imaging, extending beyond traditional enhancement and modeling tasks. Our study highlights the transformative potential of GANs in medical imaging and provides a foundation for future advancements in predictive dentistry.
DC.rights	This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
DC.format	97272
citation_title	Tooth Development Prediction Using a Generative Machine Learning Approach
citation_author	Kokomoto, Kazuma
	Okawa, Rena
	Nakano, Kazuhiko
	Nozaki, Kazunori
citation_publisher	Institute of Electrical and Electronics Engineers Inc.
citation_language	English
citation_date	2024-06-28
citation_journal_title	IEEE Access
citation_volume	12
citation_firstpage	87645
citation_lastpage	87652
citation_doi	https://doi.org/10.1109/ACCESS.2024.3416748
citation_public_url	https://hdl.handle.net/11094/97272

Tooth Development Prediction Using a Generative Machine Learning Approach

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