Autonomous control of extrusion bioprinting using convolutional neural networks

Extrusion bioprinting technology suffers from reproducibility challenges due to the open-loop nature of current hardware systems. Here, we present a novel AI-powered extrusion bioprinting platform with integrated real-time quality monitoring and automated error correction capabilities. To achieve this, we engineered a custom bioprinting system with an integrated camera for continuous process monitoring and trained convolutional neural networks (CNNs) to classify the extrusion process in real-time. The CNN models, including Xception and ResNet, were trained on a combination of real and synthetic data to classify extrusion quality (good, over, or under) across various printing scenarios, including single-line and infill patterns. Notably, transfer learning, utilizing synthetic data for initial training followed by refinement with real-world data enhanced classification accuracy, with the Xception model displaying 90% accuracy for single-line extrusion and 75% for infill extrusion. This intelligent monitoring system was then coupled with a closed-loop control system that dynamically adjusted extrusion parameters on-the-fly to correct errors. The platform successfully corrected both over- and under-extrusion errors for alginate and collagen bioinks with varying rheological properties, demonstrating adaptability to unseen materials. Importantly, extrusion errors were corrected within [~]10 seconds. This novel closed-loop bioprinting platform represents a significant advance over traditional open-loop systems.