Deep learning for fabrication and maturation of 3D bioprinted tissues and organs

• Published in: Virtual and physical prototyping Vol. 15; no. 3; pp. 340 - 358
• Main Authors: Ng, Wei Long, Chan, Alvin, Ong, Yew Soon, Chua, Chee Kai
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 02-07-2020; Taylor & Francis Group
• Subjects: 3D bioprinting; 3D printing; biofabrication; deep learning; machine learning
• ISSN: 1745-2759; 1745-2767
• DOI: 10.1080/17452759.2020.1771741

Bioprinting is a relatively new and promising tissue engineering approach to solve the problem of donor shortage for organ transplantation. It is a highly-advanced biofabrication system that enables the printing of materials in the form of biomaterials, living cells and growth factors in a layer-by-layer manner to manufacture 3D tissue-engineered constructs. The current workflow involves a myriad of manufacturing complexities, from medical image processing to optimisation of printing parameters and refinements during post-printing tissue maturation. Deep learning is a powerful machine learning technique that has fuelled remarkable progress in image and language applications over the past decade. In this perspective paper, we highlight the integration of deep learning into 3D bioprinting technology and the implementation of practical guidelines. We address potential adoptions of deep learning into various 3D bioprinting processes such as image-processing and segmentation, optimisation and in-situ correction of printing parameters and lastly refinement of the tissue maturation process. Finally, we discuss implications that deep learning has on the adoption and regulation of 3D bioprinting. The synergistic interactions among the field of biology, material and deep learning-enabled computational design will eventually facilitate the fabrication of biomimetic patient-specific tissues/organs, making 3D bioprinting of tissues/organs an impending reality.