MIT advances engineered tissue production with 3D bioprinting
The team from MIT and the Polytechnic University of Milan has developed a modular, printer-agnostic monitoring system
According to MIT, a research team from the university and the Polytechnic University of Milan (Polimi) has developed a low-cost monitoring technique that could significantly improve the reproducibility and efficiency of 3D bioprinting, advancing the production of engineered tissues for real-world medical applications.
Tissue engineering seeks to replicate the structure and function of biological tissues for use in disease modeling, drug discovery, and implantable grafts. 3D bioprinting, which uses living cells, biocompatible materials, and growth factors to build tissue structures, is central to this effort. However, current methods lack integrated process control, leading to defects, inconsistent results, and wasted materials.
“A major drawback of current 3D bioprinting approaches is that they do not integrate process control methods that limit defects in printed tissues,” said Ritu Raman, Eugene Bell Career Development Chair of Tissue Engineering and assistant professor of mechanical engineering at MIT. “Incorporating process control could improve inter-tissue reproducibility and enhance resource efficiency, for example, limiting material waste.”
To address this challenge, Raman collaborated with Polimi professor Bianca Colosimo, who spent a sabbatical at MIT working with John Hart, Class of 1922 Professor and director of the Center for Advanced Production Technologies. Together with lead authors Giovanni Zanderigo, a Rocca Fellow at Polimi, and MIT’s Ferdows Afghah, the team developed a modular, printer-agnostic monitoring system.
The technique, detailed in the journal Device, integrates a compact digital microscope to capture high-resolution images of tissue during printing. An AI-based image analysis pipeline then compares the images to the intended design in real time, allowing rapid detection of errors such as excess or insufficient bio-ink.
“This method enabled us to quickly identify print defects, thus helping us identify optimal print parameters for a variety of different materials,” said Raman. “The approach is a low-cost – less than $500 – scalable, and adaptable solution that can be readily implemented on any standard 3D bioprinter.”
Already deployed at MIT’s SHED bioprinting facilities and mirrored at Polimi, the platform creates a shared environment for data exchange and future collaboration. Beyond monitoring, the system lays the groundwork for intelligent process control – enabling real-time inspection, adaptive correction, and automated parameter tuning.
According to the researchers, this advancement could improve reproducibility, reduce material waste, and accelerate the path toward sustainable, automated tissue engineering, ultimately enhancing treatments for injuries and disease.
