A Design of Experiment to Evaluate the Printability for Bioprinting by Using Deep Learning Image Similarity.
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| Title: | A Design of Experiment to Evaluate the Printability for Bioprinting by Using Deep Learning Image Similarity. |
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| Authors: | Balters, Leon1 (AUTHOR), Reichl, Stephan1 (AUTHOR) s.reichl@tu‐braunschweig.de |
| Source: | Journal of Biomedical Materials Research, Part A. Jul2025, Vol. 113 Issue 7, p1-14. 14p. |
| Abstract: | Bioprinting is a growing area in the field of tissue engineering that offers a potential solution to the global shortage of organ transplants. Ensuring high printability is crucial for bioprinting. To better understand printability, a design of experiment model that examines printing speed and pressure in extrusion‐based printing was developed. Two biomaterials, hyaluronic acid and sodium alginate, were selected as surrogate biomaterials to understand how rheological properties play a role in printability. Various rheological aspects such as shear‐thinning behavior, viscosity, and recovery were investigated. To further evaluate printability, a new method was used that includes deep learning image similarity. The information obtained with the surrogate bioinks was then applied to another biomaterial, methacrylated hyaluronic acid, in combination with corneal keratocytes to demonstrate the successful implementation of the outcome of this design of experiment. As a result of this study, a better understanding of the rheological properties for bioprinting was achieved, leading to a next step towards improving extrusion‐based bioprinting, which can be used for a wide range of applications. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Biomedical Materials Research, Part A is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 186640026 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Design of Experiment to Evaluate the Printability for Bioprinting by Using Deep Learning Image Similarity. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Balters%2C+Leon%22">Balters, Leon</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Reichl%2C+Stephan%22">Reichl, Stephan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> s.reichl@tu‐braunschweig.de</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Biomedical+Materials+Research%2C+Part+A%22">Journal of Biomedical Materials Research, Part A</searchLink>. Jul2025, Vol. 113 Issue 7, p1-14. 14p. – Name: Abstract Label: Abstract Group: Ab Data: Bioprinting is a growing area in the field of tissue engineering that offers a potential solution to the global shortage of organ transplants. Ensuring high printability is crucial for bioprinting. To better understand printability, a design of experiment model that examines printing speed and pressure in extrusion‐based printing was developed. Two biomaterials, hyaluronic acid and sodium alginate, were selected as surrogate biomaterials to understand how rheological properties play a role in printability. Various rheological aspects such as shear‐thinning behavior, viscosity, and recovery were investigated. To further evaluate printability, a new method was used that includes deep learning image similarity. The information obtained with the surrogate bioinks was then applied to another biomaterial, methacrylated hyaluronic acid, in combination with corneal keratocytes to demonstrate the successful implementation of the outcome of this design of experiment. As a result of this study, a better understanding of the rheological properties for bioprinting was achieved, leading to a next step towards improving extrusion‐based bioprinting, which can be used for a wide range of applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Biomedical Materials Research, Part A is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=186640026 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/jbm.a.37961 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1 Titles: – TitleFull: A Design of Experiment to Evaluate the Printability for Bioprinting by Using Deep Learning Image Similarity. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Balters, Leon – PersonEntity: Name: NameFull: Reichl, Stephan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 15493296 Numbering: – Type: volume Value: 113 – Type: issue Value: 7 Titles: – TitleFull: Journal of Biomedical Materials Research, Part A Type: main |
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