AI-powered printability evaluation framework for 3D bioprinting using Hausdorff distance metrics.

Area of Science:

• Biotechnology
• Materials Science
• Tissue Engineering

Background:

• 3D bioprinting is crucial for fabricating complex biological structures.
• Optimizing bioink formulation is challenging due to intricate material property, printability, and cell viability relationships.
• Existing printability metrics like perimeter ratio (Pr) lack comprehensive geometric fidelity assessment.

Purpose of the Study:

• To develop a novel Hausdorff distance (HD) metric for accurate printability quantification in 3D bioprinting.
• To apply machine learning (ML) models for predicting bioink printability and cell viability.
• To optimize alginate-hyaluronic acid composite inks for enhanced tissue engineering applications.

Main Methods:

• Utilized Hausdorff distance (HD) to measure geometric deviation between designed and printed structures.
• Employed machine learning algorithms including Support Vector Regression (SVR), Multi-layer Perceptron (MLP), and Convolutional Neural Networks (CNN).
• Assessed printability and PC12 cell viability in alginate-hyaluronic acid composite inks.

Main Results:

• SVR accurately characterized rheological parameters (R² ≥ 0.974).
• MLP models predicted HD values (R² = 0.932) and cell viability (R² = 0.945).
• CNN achieved high accuracy (R² = 0.986) in predicting HD from images, enabling optimal bioink formulation with ≥95% cell viability and HD ≤ 0.20.

Conclusions:

• The AI-integrated approach significantly reduces bioink optimization time.
• The novel HD metric provides a more comprehensive assessment of printability.
• Optimized bioinks demonstrate high printability and excellent long-term cell viability and proliferation potential for tissue engineering.