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

アクセス数:924件（2025-10-25 07:56 集計）

固定URL: https://hdl.handle.net/11094/97272

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タイトル	Tooth Development Prediction Using a Generative Machine Learning Approach
著者	NRID 1000000803107 1000000803107 ORCID 0000-0003-4362-5433 0000-0003-4362-5433 researchmap permalink kokomoto kokomoto Scopus Author ID 57193763258 57193763258 著者名 Kokomoto, Kazuma
	NRID 1000080437384 1000080437384 researchmap permalink read0091517 read0091517 Scopus Author ID 9744705500 9744705500 ORCID 0000-0002-8634-209X 0000-0002-8634-209X 著者名 Okawa, Rena
	NRID 1000000379083 1000000379083 researchmap permalink read0202427 read0202427 Scopus Author ID 7402011667 7402011667 著者名 Nakano, Kazuhiko
	NRID 1000040379110 1000040379110 ORCID 0000-0003-3378-0915 0000-0003-3378-0915 researchmap permalink dentgrid dentgrid Scopus Author ID 36665004400 36665004400 著者名 Nozaki, Kazunori
キーワード等	Artificial intelligence
	dental informatics
	machine learning
	medical informatics applications
	orthodontics
	pediatric dentistry
内容	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.
抄録	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.
出版者	Institute of Electrical and Electronics Engineers Inc.
収録物名	IEEE Access
巻(号)	12
ページ	87645-87652
刊行年月	2024-06-28
言語	英語
ハンドルURL	https://hdl.handle.net/11094/97272
eISSN	21693536
関連情報 (isIdenticalTo)	DOI (出版社版) https://doi.org/10.1109/ACCESS.2024.3416748
アクセス権	open access
権利情報	This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
出版タイプ	Version of Record
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
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	英語
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	英語
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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