Artificial Intelligence‐Assisted High‐Throughput Screening of Printing Conditions of Hydrogel Architectures for Accelerated Diabetic Wound Healing

Abstract In 3D (bio)printing, it is critical to optimize the printing conditions to obtain scaffolds with designed structures and good uniformities. Traditional approaches for optimizing the parameters oftentimes rely on the prior knowledge of the operators and tedious optimization experiments, which can be both time‐consuming and labor‐intensive. Moreover, with the rapid increase in the types of biomaterial inks and the geometrical complexities of the scaffolds to be fabricated, such a traditional strategy may prove less effective. To address the challenge, an artificial intelligence‐assisted high‐throughput printing‐condition‐screening system (AI‐HTPCSS) is proposed, which is composed of a programmable pneumatic extrusion (bio)printer and an AI‐assisted image‐analysis algorithm. Based on the AI‐HTPCSS, the printing conditions for obtaining uniformly structured hydrogel architectures are screened in a high‐throughput manner. The results show that the scaffolds printed under the optimized conditions demonstrate satisfying mechanical properties, in vitro biological performances, and efficacy in accelerating the diabetic wound healing in vivo. The unique AI‐HTPCSS is expected to offer an enabling platform technology on streamlining the manufacturing of tissue‐engineering scaffolds through 3D (bio)printing techniques in the future.