Related Experiment Video
Updated: Mar 25, 2026
07:17
Author Spotlight: Understanding Chronic Lung Diseases Using 3D Printed Phototunable Hydrogels
Published on: June 30, 2023
2.5K
High-fidelity 'top-down' DLP bioprinting of multi-material soft tissue constructs enabled by computer vision-based
Nadina Aimé Usseglio1, Alejandro González-Santos1,2, Alba Fernandez Ferrer1
1Advanced Biofabrication Laboratory-DNIETO LAB, Centro Interdisciplinar de Química e Bioloxía, CICA, Universidade da Coruña. A Coruña, Spain, Spain.
Biofabrication
|March 23, 2026
Summary
This study introduces an automated platform for precise layer thickness control in multi-material bioprinting, enhancing tissue engineering. The vision-based system achieves sub-0.1 mm accuracy, improving reproducibility and scalability for regenerative medicine applications.
Area of Science:
- Regenerative Medicine
- Bioprinting Technologies
- Tissue Engineering
Background:
- Bioprinting enables complex 3D tissue fabrication but faces challenges in multi-material integration and structural fidelity.
- Precise layer thickness control is critical in top-down vat photopolymerization bioprinting, particularly Digital Light Processing (DLP), affecting mechanical integrity and resolution.
Purpose of the Study:
- To develop and validate a novel automated platform for real-time measurement of the dynamic gap in multi-material top-down DLP bioprinting.
- To overcome limitations in precise layer thickness control, a key bottleneck in functional tissue engineering.
Main Methods:
- Comparative assessment of classical computer vision and deep learning (CNN-based) techniques for dynamic gap measurement.
- Development of a vision-based system for automated, real-time layer thickness monitoring during bioprinting.
- Testing system adaptability across various bioinks with different viscosities, opacities, and photopolymerization kinetics.
Main Results:
- Achieved sub-0.1 mm precision (0.092 mm) in layer thickness measurement with high correlation to mechanical measurements (R = 0.994).
- Demonstrated adaptability to diverse bioinks without manual recalibration.
- Successfully bioprinted multi-material vascular-like and skin tissue models with high spatial fidelity and compartmentalization.
Conclusions:
- The developed vision-based platform significantly improves reproducibility, material adaptability, and structural precision in multi-material bioprinting.
- This advancement addresses critical bottlenecks, paving the way for clinically scalable tissue manufacturing systems.
- The system enhances biofabrication workflows for creating functional tissue constructs for regenerative medicine.
