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Related Experiment Video

Updated: Sep 6, 2025

Automated Robotic Dispensing Technique for Surface Guidance and Bioprinting of Cells
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Bioink Formulation and Machine Learning-Empowered Bioprinting Optimization.

Sebastian Freeman1, Stefano Calabro1, Roma Williams1,2

  • 1Department of Biomedical Engineering, Binghamton University, Binghamton, NY, United States.

Frontiers in Bioengineering and Biotechnology
|July 5, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) accelerates bioprinting by optimizing bioink formulation and enabling real-time error detection. This review explores ML

Keywords:
additive biomanufacturingbiofabricationbioinkbioink formationbiomaterialsbioprintingmachine learningtissue engineering

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Area of Science:

  • Bioprinting and Tissue Engineering
  • Biomaterials Science
  • Machine Learning Applications

Background:

  • Bioprinting fabricates complex tissues using robotic cell and biomaterial placement.
  • Advances in bioprinting, materials, and strategies improve tissue mimicry and architecture.
  • Machine learning (ML) is emerging as a transformative tool in bioprinting.

Purpose of the Study:

  • To review current trends in bioink formulation.
  • To explore the application of ML in optimizing bioprinting processes.
  • To examine the role of rheological properties in bioink printability.

Main Methods:

  • Review of literature on bioink formulation and ML in bioprinting.
  • Analysis of rheometric properties (e.g., viscosity, shear moduli, yield shear stress).
  • Survey of ML applications in surgical site bioprinting, AI printing, and post-printing optimization.

Main Results:

  • ML accelerates bioink formulation optimization and error detection.
  • Rheological properties significantly influence bioink printability.
  • ML enhances precision in surgical site bioprinting and closed-loop printing.

Conclusions:

  • ML integration represents a new paradigm for advancing bioprinting.
  • Understanding material properties is crucial for successful bioink development.
  • ML offers significant potential for improving bioprinting efficiency and outcomes.