Modular and AI-driven in situ monitoring platform for real-time process analysis in embedded bioprinting

Real-time monitoring and in situ data analysis are increasingly vital for enhancing precision, reproducibility, and defect detection in embedded bioprinting. Accessible strategies for integrating real-time sensing and analysis are thus becoming essential to ensure consistent print quality and optimize print parameters. We have developed a modular, low-cost, and printer-agnostic platform that combines a compact sensing architecture with an AI-based image-analysis pipeline to enable in situ process monitoring, defect detection, and print quality assessment. We demonstrate that 2D in situ images provide reliable approximations of 3D filament geometries, reveal pressure-related effects on filament diameters, and identify critical velocity thresholds for printing stability of different acellular and cellular bioinks. Together, these findings establish our approach as a low-cost, scalable, and adaptable solution that can be readily implemented across a range of embedded bioprinting workflows, offering a practical path toward greater reproducibility and automation.