The field of tissue engineering aims to replicate the structure and function of real biological tissues. This engineered tissue has potential applications in disease modeling, drug discovery, and implantable grafts.
3D bioprinting, which uses living cells, biocompatible materials, and growth factors to build three-dimensional tissue and organ structures, has emerged as a key tool in the field. To date, one of the most-used approaches for bioprinting relies on additive manufacturing techniques and digital models, depositing 2D layers of bio-inks, composed of cells in a soft gel, into a support bath, layer-by-layer, to build a 3D structure. While these techniques do enable fabrication of complex architectures with features that are not easy to build manually, current approaches have limitations.
“A major drawback of current 3D bioprinting approaches is that they do not integrate process control methods that limit defects in printed tissues. Incorporating process control could improve inter-tissue reproducibility and enhance resource efficiency, for example limiting material waste,” says Ritu Raman, the Eugene Bell Career Development Chair of Tissue Engineering and an assistant professor of mechanical engineering.
She adds, “given the diverse array of available 3D bioprinting tools, there is a significant need to develop process optimization techniques that are modular, efficient, and accessible.”
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The need motivated Raman to collaborate with Tolga Durak at MIT’s Safety, Health, Environmental Discovery Lab (The SHED) and seek the expertise of Professor Bianca Colosimo of the Polytechnic University of Milan, also known as Polimi. Colosimo recently completed a sabbatical at MIT, which was hosted by John Hart, Class of 1922 Professor, co-director of MIT’s Initiative for New Manufacturing, director of the Center for Advanced Production Technologies, and head of the Department of Mechanical Engineering.
“Artificial Intelligence and data mining are already reshaping our daily lives, and their impact will be even more profound in the emerging field of 3D bioprinting, and in manufacturing at large,” says Colosimo. During her MIT sabbatical, she collaborated with Raman and her team to co-develop a solution that represents a first step toward intelligent bioprinting.
“This solution is now available in both our labs at Polimi and MIT, serving as a twin platform to exchange data and results across different environments and paving the way for many new joint projects in the years to come,” Colosimo says.
A new paper by Raman, Colosimo, and lead authors Giovanni Zanderigo, a Rocca Fellow at Polimi, and Ferdows Afghah of MIT published this week in the journal Device presents a novel technique that addresses this challenge. The team built and validated a modular, low-cost, and printer-agnostic monitoring technique that integrates a compact tool for layer-by-layer imaging. In their method, a digital microscope captures high-resolution images of tissues during printing and rapidly compares them to the intended design with an AI-based image analysis pipeline.
“This method enabled us to quickly identify print defects, such as depositing too much or too little bio-ink, thus helping us identify optimal print parameters for a variety of different materials,” says Raman. “The approach is a low-cost — less than $500 — scalable, and adaptable solution that can be readily implemented on any standard 3D bioprinter. Here at MIT, the monitoring platform has already been integrated into the 3D bioprinting facilities in The SHED. Beyond MIT, our research offers a practical path toward greater reproducibility, improved sustainability, and automation in the field of tissue engineering. This research could have a positive impact on human health by improving the quality of the tissues we fabricate to study and treat debilitating injuries and disease.”
“These capabilities will be immediately utilized for fabricating the bio-printed scaffolds and vascularized constructs to cultivate microphysiological systems (MPS) and large-scale liver organoids in our current research project,” says Durak, highlighting the significance of this process optimization.
The authors indicate that the new method is more than a monitoring tool. It also serves as a foundation for intelligent process control in embedded bioprinting. By enabling real-time inspection, adaptive correction, and automated parameter tuning, the researchers anticipate that the approach can improve reproducibility, reduce material waste, and accelerate process optimization for real-world applications in tissue engineering.
Source: Massachusetts Institute of Technology.
Published on September 23, 2025