Research Gallery (研究ギャラリー)

3D Bioprinting (3Dバイオプリンティング)

AI (人工知能)

Research Related (研究関連)

Generative AI-guided in silico closed-loop optimisation of deposition morphology for 3D bioprinting applications (2026/05/21)

Colin Zhang, Kelum Elvitigala, and Shinji Sakai. Generative AI-guided in silico closed-loop optimisation of deposition morphology for 3D bioprinting applications. Virtual and Physical Prototyping 21, e2671497 (2026).
https://doi.org/10.1080/17452759.2026.2671497. Open Access.

Publisher Link: https://www.tandfonline.com/doi/full/10.1080/17452759.2026.2671497
GitHub Repository: https://github.com/KORINZ/generative-ai-bioprinting-framework
Data Repository: https://doi.org/10.5281/zenodo.19602891
Supplemental: https://www.tandfonline.com/doi/suppl/10.1080/17452759.2026.2671497

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AI-powered printability evaluation framework for 3D bioprinting using Hausdorff distance metrics (2025/12/17)

Colin Zhang, Kelum Elvitigala, and Shinji Sakai. AI-powered printability evaluation framework for 3D bioprinting using Hausdorff distance metrics . Biofabrication 18, 015015 (2026).
https://doi.org/10.1088/1758-5090/ae288c. Subscription Access.

Publisher Link: https://iopscience.iop.org/article/10.1088/1758-5090/ae288c
GitHub Repository: https://github.com/KORINZ/printability-ai
Supplemental: https://iopscience.iop.org/article/10.1088/1758-5090/ae288c/data

After the embargo period (2026/12/17), the accepted manuscript will be made available in the institutional repository: https://hdl.handle.net/11094/104697.

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Machine learning-based prediction and optimisation framework for as-extruded cell viability in extrusion-based 3D bioprinting (2024/09/11)

Colin Zhang, Kelum Elvitigala, Wildan Mubarok, Yasunori Okano, and Shinji Sakai. Machine learning-based prediction and optimisation framework for as-extruded cell viability in extrusion-based 3D bioprinting. Virtual and Physical Prototyping 19, e2400330 (2024).
https://doi.org/10.1080/17452759.2024.2400330. Open Access.

Publisher Link: https://www.tandfonline.com/doi/full/10.1080/17452759.2024.2400330
GitHub Repository: https://github.com/KORINZ/in-silico-bioink-viability-prediction

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化学工学会第55回秋季大会プレスリリース 注目講演 [1130件中の22件] (2024/08/28)

【第55回秋季大会】注目講演・注目セッションを選定

https://www.scej.org/news/detail/23235