AI-powered printability evaluation framework for 3D bioprinting using Hausdorff distance metrics

Zhang, Colin; Kelum Chamara Manoj Lakmal Elvitigala; Sakai, Shinji

doi:https://doi.org/10.1088/1758-5090/ae288c

AI-powered printability evaluation framework for 3D bioprinting using Hausdorff distance metrics

Zhang, Colin; Kelum Chamara Manoj Lakmal Elvitigala; Sakai, Shinji

Biofabrication, 2025, 18, 015015

アクセス数:40件（2026-05-16 01:22 集計）

固定URL: https://hdl.handle.net/11094/104697

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タイトル	AI-powered printability evaluation framework for 3D bioprinting using Hausdorff distance metrics
著者	著者名 Zhang, Colin
	NRID 1000061011846 1000061011846 ORCID 0000-0002-9649-0635 0000-0002-9649-0635 researchmap permalink kelum kelum Scopus Author ID 57207876505 57207876505 著者名 Kelum Chamara Manoj Lakmal Elvitigala
	NRID 1000020359938 1000020359938 ORCID 0000-0002-1041-4798 0000-0002-1041-4798 researchmap permalink sakaishinji sakaishinji Scopus Author ID 35450082900 35450082900 著者名 Sakai, Shinji
キーワード等	3D bioprinting
	additive manufacturing
	convolutional neural network
	Hausdorff distance
	machine learning
	printability
	rheology
内容	Zhang C., Elvitigala K., Sakai, S.. AI-powered printability evaluation framework for 3D bioprinting using Hausdorff distance metrics. Biofabrication 18, 015015 (2026).
内容	This is the Accepted Manuscript version of an article accepted for publication in Biofabrication. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/1758-5090/ae288c.
抄録	3D bioprinting enables rapid fabrication of complex biological structures for tissue engineering applications. However, optimizing bioink formulation remains challenging due to complex relationships among material properties, printability, and cell viability. While the perimeter ratio (Pr) is commonly used to evaluate printability, it cannot adequately capture the full geometric fidelity required for comprehensive printability assessments, thereby limiting robust bioink design. To address this limitation, a novel Hausdorff distance (HD) metric is employed to quantify printability, directly measuring the maximum deviation between the designed and printed structures. Furthermore, multiple machine-learning approaches were applied to alginate-hyaluronic acid composite inks and rat pheochromocytoma-derived PC12 cells to assess printability and cell viability. Rheological parameters were characterized using support vector regression (SVR) with R² ≧ 0.974. Multi-layer perceptron (MLP) regressors achieved R² values of 0.932 and 0.945 when predicting HD values of printed grid structures and cell viability, respectively. A regression-based convolutional neural network (CNN) was developed to predict HD values directly from grid structure images, achieving an R² of 0.986. Through optimization, optimal as-extruded cell viability (≧95%) was achieved while maintaining high printability (HD ≦ 0.20). The optimal ink composition was further demonstrated with good long-term cell viability and proliferation potential. This proposed AI-integrated approach can dramatically reduce ink optimization time by rapidly predicting rheological properties, printability, and cell viability from minimal experimental data.
出版者	Institute of Physics
収録物名	Biofabrication
巻(号)	18
ページ	015015
刊行年月	2025-12-17
言語	英語
ハンドルURL	https://hdl.handle.net/11094/104697
PISSN	17585082
eISSN	17585090
関連情報 (isVersionOf)	DOI (出版社版) https://doi.org/10.1088/1758-5090/ae288c
アクセス権	embargoed access
権利情報	This Accepted Manuscript is available for reuse under a CC BY-NC-ND licence after the 12 month embargo period provided that all the terms of the licence are adhered to. The full-text file will be made open to the public on 17 December 2026 in accordance with the publisher's policy.
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
DCMI資源タイプ	text
DCTERMS.bibliographicCitation	Biofabrication. 2025, 18, p. 015015
DC.title	AI-powered printability evaluation framework for 3D bioprinting using Hausdorff distance metrics
DC.creator	Zhang, Colin
	Kelum Chamara Manoj Lakmal Elvitigala
	Sakai, Shinji
DC.publisher	Institute of Physics
DC.language' scheme='DCTERMS.RFC1766	英語
DC.type' scheme='DCTERMS.DCMIType	text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2025-12-17
DC.identifier	https://doi.org/10.1088/1758-5090/ae288c
DC.subject	3D bioprinting
	additive manufacturing
	convolutional neural network
	Hausdorff distance
	machine learning
	printability
	rheology
DC.description	Zhang C., Elvitigala K., Sakai, S.. AI-powered printability evaluation framework for 3D bioprinting using Hausdorff distance metrics. Biofabrication 18, 015015 (2026).
DC.description	This is the Accepted Manuscript version of an article accepted for publication in Biofabrication. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/1758-5090/ae288c.
DCTERMS.abstract	3D bioprinting enables rapid fabrication of complex biological structures for tissue engineering applications. However, optimizing bioink formulation remains challenging due to complex relationships among material properties, printability, and cell viability. While the perimeter ratio (Pr) is commonly used to evaluate printability, it cannot adequately capture the full geometric fidelity required for comprehensive printability assessments, thereby limiting robust bioink design. To address this limitation, a novel Hausdorff distance (HD) metric is employed to quantify printability, directly measuring the maximum deviation between the designed and printed structures. Furthermore, multiple machine-learning approaches were applied to alginate-hyaluronic acid composite inks and rat pheochromocytoma-derived PC12 cells to assess printability and cell viability. Rheological parameters were characterized using support vector regression (SVR) with R² ≧ 0.974. Multi-layer perceptron (MLP) regressors achieved R² values of 0.932 and 0.945 when predicting HD values of printed grid structures and cell viability, respectively. A regression-based convolutional neural network (CNN) was developed to predict HD values directly from grid structure images, achieving an R² of 0.986. Through optimization, optimal as-extruded cell viability (≧95%) was achieved while maintaining high printability (HD ≦ 0.20). The optimal ink composition was further demonstrated with good long-term cell viability and proliferation potential. This proposed AI-integrated approach can dramatically reduce ink optimization time by rapidly predicting rheological properties, printability, and cell viability from minimal experimental data.
DC.rights	This Accepted Manuscript is available for reuse under a CC BY-NC-ND licence after the 12 month embargo period provided that all the terms of the licence are adhered to. The full-text file will be made open to the public on 17 December 2026 in accordance with the publisher's policy.
DC.format	104697
citation_title	AI-powered printability evaluation framework for 3D bioprinting using Hausdorff distance metrics
citation_author	Zhang, Colin
	Kelum Chamara Manoj Lakmal Elvitigala
	Sakai, Shinji
citation_publisher	Institute of Physics
citation_language	英語
citation_date	2025-12-17
citation_journal_title	Biofabrication
citation_volume	18
citation_firstpage	015015
citation_doi	https://doi.org/10.1088/1758-5090/ae288c
citation_public_url	https://hdl.handle.net/11094/104697

AI-powered printability evaluation framework for 3D bioprinting using Hausdorff distance metrics

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