Enhancing quality control in bioprinting through machine learning.

Area of Science:

• Bioprinting
• Tissue Engineering
• Regenerative Medicine

Background:

• Bioprinting extensively studied for tissue engineering, but clinical translation is limited.
• Technological challenges include complexity replication, printing time, and biomaterial choice.
• Regulatory barriers and lack of clear product classification hinder clinical use.

Purpose of the Study:

• To review machine learning (ML) applications in bioprinting quality control (QC).
• To analyze ML models for QC in bioprinting from a technical perspective.
• To provide insights into challenges and future directions for ML in bioprinting QC.

Main Methods:

• Comprehensive literature analysis of ML-based QC solutions in bioprinting.
• Evaluation of ML models based on data used, algorithms, and performance metrics.
• Assessment of ML's role in pre-process, in-process, and post-process QC.

Main Results:

• ML algorithms show promise for automating bioprinting QC.
• ML can reduce inter-batch variability and enhance product consistency.
• Identified various ML models and their technical performance in bioprinting QC.

Conclusions:

• ML is a key technology for advancing bioprinting QC.
• Automated QC through ML can accelerate clinical translation and commercialization.
• Further research is needed to overcome current challenges in ML-driven bioprinting QC.