High-fidelity “top-down” DLP bioprinting of multi-material soft tissue constructs enabled by computer vision–based layer control

Bioprinting continues to redefine the frontiers of regenerative medicine by enabling the fabrication of complex, three-dimensional tissue constructs that emulate native biological and mechanical functions. However, despite significant progress, critical challenges remain, particularly in achieving precise multi-material integration, high-resolution patterning, and structural fidelity necessary for functional tissue engineering. A major limitation in "top-down" vat photopolymerization bioprinting, especially Digital Light Processing (DLP)-based approaches, lies in the precise control of layer thickness, a parameter that directly affects mechanical integrity, biological activity, and spatial resolution. This study presents a novel, automated platform designed to overcome one of the most persistent bottlenecks in multi-material top-down DLP bioprinting: the real-time, accurate measurement of the dynamic gap between the cured layer and the bioink surface. Through a comparative assessment of classical computer vision and deep learning (CNN-based) techniques, we demonstrate a system capable of achieving sub-0.1 mm precision (0.092 mm) with strong correlation to mechanical measurements (R = 0.994). This vision-based system adapts to a wide range of bioinks with varying viscosities, opacities, and photopolymerization kinetics, eliminating the need for manual recalibration during material switching. As a demonstration of its capabilities, we successfully printed a multimaterial vascular-like tissue structure with high spatial fidelity across heterogeneous biomaterials. Further, we bioprinted a multimaterial skin tissue model, featuring compartmentalized dermal/bone analogs, to enable in vitro functional evaluation. These case studies highlight the platform's potential to advance biofabrication workflows by improving reproducibility, material adaptability, and structural precision, paving the way toward clinically scalable tissue manufacturing systems.&#xD.