The Histological Detection of Ulcerative Colitis Using a No-Code Artificial Intelligence Model

Hamamoto, Yuichiro; Kawamura, Michihiro; Uchida, Hiroki; Hiramatsu, Kazuhiro; Katori, Chiaki; Asai, Hinako; Shimizu, Shigeki; Egawa, Satoshi; Yoshida, Kyotaro

doi:https://doi.org/10.1177/10668969231204955

The Histological Detection of Ulcerative Colitis Using a No-Code Artificial Intelligence Model

Hamamoto, Yuichiro; Kawamura, Michihiro; Uchida, Hiroki et al.

International Journal of Surgical Pathology, 2024, 32(5), 890-894

アクセス数:782件（2026-02-15 22:28 集計）

固定URL: https://hdl.handle.net/11094/98831

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タイトル	The Histological Detection of Ulcerative Colitis Using a No-Code Artificial Intelligence Model
著者	ORCID 0000-0002-3511-760X 0000-0002-3511-760X researchmap permalink yhama yhama Scopus Author ID 57210885924 57210885924 著者名 Hamamoto, Yuichiro
	著者名 Kawamura, Michihiro
	著者名 Uchida, Hiroki
	著者名 Hiramatsu, Kazuhiro
	著者名 Katori, Chiaki
	著者名 Asai, Hinako
	NRID 1000030336672 1000030336672 著者名 Shimizu, Shigeki
	著者名 Egawa, Satoshi
	著者名 Yoshida, Kyotaro
キーワード等	adenocarcinoma
	artificial intelligence
	inflammatory bowel disease
	machine learning
	ulcerative colitis
内容	Hamamoto Y., Kawamura M., Uchida H., et al. The Histological Detection of Ulcerative Colitis Using a No-Code Artificial Intelligence Model. International Journal of Surgical Pathology 32(5), 890-894 © 2023 The Author(s). DOI: 10.1177/10668969231204955.
抄録	Ulcerative colitis (UC) is an intractable disease that affects young adults. Histological findings are essential for its diagnosis; however, the number of diagnostic pathologists is limited. Herein, we used a no-code artificial intelligence (AI) platform “Teachable Machine” to train a model that could distinguish between histological images of UC, non-UC coloproctitis, adenocarcinoma, and control. A total of 5100 histological images for training and 900 histological images for testing were prepared by pathologists. Our model showed accuracies of 0.99, 1.00, 0.99, and 0.99, for UC, non-UC coloproctitis, adenocarcinoma, and control, respectively. This is the first report in which a no-code easy AI platform has been able to comprehensively recognize the distinctive histologic patterns of UC.
出版者	SAGE Publications Inc.
収録物名	International Journal of Surgical Pathology
巻(号)	32(5)
ページ	890-894
刊行年月	2024-08-01
言語	英語
ハンドルURL	https://hdl.handle.net/11094/98831
PISSN	10668969
eISSN	19402465
関連情報 (isVersionOf)	DOI (出版社版) https://doi.org/10.1177/10668969231204955
アクセス権	open access
出版タイプ	Accepted Manuscript
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	International Journal of Surgical Pathology. 2024, 32(5), p. 890-894
DC.title	The Histological Detection of Ulcerative Colitis Using a No-Code Artificial Intelligence Model
DC.creator	Hamamoto, Yuichiro
	Kawamura, Michihiro
	Uchida, Hiroki
	Hiramatsu, Kazuhiro
	Katori, Chiaki
	Asai, Hinako
	Shimizu, Shigeki
	Egawa, Satoshi
	Yoshida, Kyotaro
DC.publisher	SAGE Publications Inc.
DC.language' scheme='DCTERMS.RFC1766	英語
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2024-08-01
DC.identifier	https://doi.org/10.1177/10668969231204955
DC.subject	adenocarcinoma
	artificial intelligence
	inflammatory bowel disease
	machine learning
	ulcerative colitis
DC.description	Hamamoto Y., Kawamura M., Uchida H., et al. The Histological Detection of Ulcerative Colitis Using a No-Code Artificial Intelligence Model. International Journal of Surgical Pathology 32(5), 890-894 © 2023 The Author(s). DOI: 10.1177/10668969231204955.
DCTERMS.abstract	Ulcerative colitis (UC) is an intractable disease that affects young adults. Histological findings are essential for its diagnosis; however, the number of diagnostic pathologists is limited. Herein, we used a no-code artificial intelligence (AI) platform “Teachable Machine” to train a model that could distinguish between histological images of UC, non-UC coloproctitis, adenocarcinoma, and control. A total of 5100 histological images for training and 900 histological images for testing were prepared by pathologists. Our model showed accuracies of 0.99, 1.00, 0.99, and 0.99, for UC, non-UC coloproctitis, adenocarcinoma, and control, respectively. This is the first report in which a no-code easy AI platform has been able to comprehensively recognize the distinctive histologic patterns of UC.
DC.format	98831
citation_title	The Histological Detection of Ulcerative Colitis Using a No-Code Artificial Intelligence Model
citation_author	Hamamoto, Yuichiro
	Kawamura, Michihiro
	Uchida, Hiroki
	Hiramatsu, Kazuhiro
	Katori, Chiaki
	Asai, Hinako
	Shimizu, Shigeki
	Egawa, Satoshi
	Yoshida, Kyotaro
citation_publisher	SAGE Publications Inc.
citation_language	英語
citation_date	2024-08-01
citation_journal_title	International Journal of Surgical Pathology
citation_volume	32
citation_issue	5
citation_firstpage	890
citation_lastpage	894
citation_doi	https://doi.org/10.1177/10668969231204955
citation_public_url	https://hdl.handle.net/11094/98831

The Histological Detection of Ulcerative Colitis Using a No-Code Artificial Intelligence Model

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