October 1, 2024

Machine Learning Enhanced High Fidelity and High Viability Bioprinting using Rheological and Compositional Predictors

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High cell viability was achieved in 3D bioprinted structures, mitigating cell death during printing.
A hyperparameter-tuned machine learning model predicted optimal formulations to improve bioprinting outcomes.
Collagen-based bioinks reinforced with alginates and nanoparticles offered a promising solution for material challenges.
Design of Experiment methodology allowed systematic exploration of material variations, enhancing bioprinting precision.

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Implication

This study demonstrates enhanced cell viability and structural fidelity in 3D bioprinting, suggesting machine learning can optimize bioink compositions.

Cite This Study

Qavi et al. (2024) studied this question.

synapsesocial.com/papers/68af7f467567bf4f94ff668f https://doi.org/10.21872/2024iise_7125

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Implication

This study demonstrates enhanced cell viability and structural fidelity in 3D bioprinting, suggesting machine learning can optimize bioink compositions.

Machine Learning Enhanced High Fidelity and High Viability Bioprinting using Rheological and Compositional Predictors

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Discussion

Implication

Cite This Study

Machine Learning Enhanced High Fidelity and High Viability Bioprinting using Rheological and Compositional Predictors

Key Points

Discussion

Implication

Cite This Study

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