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Related Experiment Video

Updated: Jul 7, 2025

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Cell viability prediction and optimization in extrusion-based bioprinting via neural network-based Bayesian

Dorsa Mohammadrezaei1, Lena Podina2, Johanna De Silva1

  • 1Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada.

Biofabrication
|December 21, 2023
PubMed
Summary

This study introduces machine learning models to predict cell viability in 3D bioprinting, optimizing conditions for regenerative medicine and cancer models. The models significantly improve prediction accuracy, reducing costly trial-and-error experiments.

Keywords:
bioprintingcell_viability optimizationcell_viability predictionextrusion_based bioprintingmachine learningneural networkneural network-based Bayesian optimization model

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Area of Science:

  • Biotechnology
  • Regenerative Medicine
  • Biofabrication

Background:

  • 3D bioprinting is crucial for regenerative medicine and cancer modeling.
  • High cell viability in 3D bioprinting is essential for model accuracy and experimental validity.
  • Current optimization methods rely on time-consuming trial-and-error experiments.

Purpose of the Study:

  • To develop machine learning models for predicting cell viability in 3D bioprinting.
  • To create a novel optimization strategy for bioprinting parameters to maximize cell viability.
  • To reduce the reliance on empirical optimization methods in 3D bioprinting.

Main Methods:

  • Compiled a dataset of bioprinting parameters and cell viability for gelatin and alginate bioinks.
  • Developed and trained neural network models to predict cell viability.
  • Integrated Bayesian optimization with a regression neural network for parameter optimization.

Main Results:

  • Achieved a regression R2 value of 0.71 and classification accuracy of 0.86 with the trained neural network.
  • Demonstrated superior predictive performance compared to existing models.
  • Experimentally validated the effectiveness of the Bayesian optimization strategy.

Conclusions:

  • Machine learning models offer a powerful tool for predicting and optimizing cell viability in 3D bioprinting.
  • The proposed Bayesian optimization strategy effectively maximizes cell viability, minimizing experimental iterations.
  • This approach advances the efficiency and reliability of 3D bioprinting for biomedical applications.