AccScience Publishing / IJB / Online First / DOI: 10.36922/IJB026320336
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RESEARCH ARTICLE

Predictive machine learning models for printability assessment in extrusion-based 3D bioprinting

Joan. C. Isichei1 ,  Siyuan Li2 ,  Joshua Copus2 ,  Sang Jin Lee2 ,  Izabele Heyward1 ,  Salil Desai1,2*
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1 Department of Industrial and Systems Engineering, College of Engineering, North Carolina Agricultural and Technical State University, Greensboro, North Carolina , United States of America
2 Wake Forest Institute for Regenerative Medicine, School of Medicine, Wake Forest University, Winston-Salem, North Carolina , United States of America
IJB, 026320336 https://doi.org/10.36922/IJB026320336
Received: 4 August 2026 | Revised: 4 September 2026 | Accepted: 9 September 2026 | Published online: 9 September 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Bioprinting has great potential in tissue engineering and regenerative medicine applications due to the possibility of creating functional tissue replacements for damaged tissues or organs. However, current methods for optimizing printing parameters are time-consuming and heavily rely on operators’ experience. In this study, Pluronic F127 and sodium alginate–Laponite (Alg-Lap) bioinks were used to evaluate the quality of four bioprinted constructs: tubular, angular, crosshatch, and overhang structures. Machine learning (ML) algorithms, including multilayer perceptron (MLP), random forest (RF), Gaussian naive Bayes (GNB), classification and regression trees (CART), support vector machines (SVM), and logistic regression (LR), were evaluated for classifying constructs into print-quality categories using pre-print parameters and post-print geometry-specific quality measurements. RF performed best for the tubular, crosshatch, and overhang structures, achieving classification accuracies of 91.9%, 95.3%, and 96.0%, respectively, while MLP performed best for the angular structure, achieving 96.3% accuracy. Balanced accuracies for these best-performing models were 85.8%, 91.8%, 86.9%, and 90.8% for the tubular, crosshatch, overhang, and angular structures, respectively. These findings demonstrate the potential of ML-based quality classification to support more reproducible assessment of extrusion-based bioprinting outcomes, advancing tissue engineering and regenerative medicine applications while enhancing efficiency and reproducibility.

Keywords
3D bioprinting
Bioinks
Machine learning
Predictive models
Printability
Funding
The authors would like to express their gratitude for funding support from the National Science Foundation Grant (NSF Engines Award #2315654).
Conflict of interest
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
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International Journal of Bioprinting, Electronic ISSN: 2424-8002 Print ISSN: 2424-7723, Published by AccScience Publishing
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