Employing artificial intelligence to augment 3D bioprinting

3D bioprinting is emerging as a powerful tool for fabricating tissues and even entire organs for research, testing, and implantation. The ability to additively deposit cells, hydrogels, and other biomaterials to create customized structures can confer tremendous advantages during research, but this results in significant complexity as well.

Chen et al. discussed applications of artificial intelligence (AI) in 3D bioprinting, outlining tools and methods to tackle the intricacies of the field and produce valuable results.

AI can analyze complicated data, produce easily understandable outputs, and perform complex calculations that would otherwise be laborious and time-consuming. These traits can be employed to solve many longstanding problems in 3D bioprinting.

“Common applications of AI in bioprinting include medical image reconstruction, where AI algorithms automate the segmentation and modeling of anatomical structures; bioink selection, where machine learning predicts optimal formulations for printability and biocompatibility; and real-time monitoring and adjustment of printing parameters, ensuring high fidelity and functionality of the printed structures,” said author Jie Huang.

The authors hope their overview will encourage more researchers to harness the potential of AI, unlocking new techniques and applications for bioprinting technology to benefit scientific understanding and clinical practice.

“Advancements in automating the entire bioprinting process, including in situ printing, are promising,” said Huang. “AI can automate these processes to achieve more consistent and precise outcomes, reducing human error and increasing efficiency. In situ bioprinting, which involves printing tissues and structures directly within the body, has revolutionary potential for repairing and regenerating damaged tissues in real-time during surgical procedures.”

Source: “Recent advances and applications of artificial intelligence in 3D bioprinting,” by Hongyi Chen, Bin Zhang, and Jie Huang, Biophysics Reviews (2024). The article can be accessed at https://doi.org/10.1063/5.0190208 .