Machine Learning & Bioprinting: Researchers Improve Drop-on-Demand (DOD) Methods

As bioprinting continues to pick up steam in labs around the world, researchers still study the process intensively to build on current techniques and innovation. In ‘Multi-Objective Optimization Design through Machine Learning for Drop-on-Demand Bioprinting,’ authors Jia Shi, Jinchun Song, Bin Song, and Wen Lu explore the challenges in drop-on-demand (DOD) methods for printing cells.

While DOD bioprinting offers major advantages such as affordability and speed in production during tissue engineering, there are other significant challenges which have been difficult to overcome in the lab, such as satellite generation, and droplets that are either too large, or speed that is too low. Tissue engineering is an extremely intricate process and keeping cells alive can be a tremendous task, so any techniques that reduce stability or accuracy are often quickly dismissed. Here, the authors detail their new design method for DOD printing parameters: multi-objective optimization (MOO).

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