Enabling Real-Time Process Analysis in Embedded Bioprinting with a Modular In Situ Monitoring Platform

Abstract

Real-time monitoring and in situ data analysis are increasingly vital for enhancing precision, reproducibility, and defect detection in embedded bioprinting. As interest grows in improving the capabilities of existing bioprinting systems, accessible strategies for integrating real-time sensing and analysis are becoming essential to ensure consistent quality and to optimize printing parameters. Here, we present a modular, low-cost, and printer-agnostic platform that combines a compact sensing architecture with an effective image analysis pipeline to enable in situ process monitoring, defect detection, and print quality assessment. the platform integrates a digital microscope aligned on-axis with the extrusion printhead to capture high-resolution images during fabrication. We applied and compared two segmentation methods, thresholding and the Segment Anything Model (SAM) on in situ and ex situ datasets acquired via confocal fluorescence imaging, finding SAM to yield stronger correlations (R = 0.85-0.86) between in situ and ex situ measurements. Additionally, we demonstrated that 2D in situ images provide reliable approximations of 3D filament geometries, supporting their use for real-time morphological assessment. the system also revealed pressure-related effects on the diameters, and a critical velocity threshold for printing stability, highlighting its value for process optimization. together, these findings establish the approach as a low-cost, scalable and adaptable solution that can be readily implemented across embedded bioprinting workflows, offering a practical path toward greater reproducibility and automation.