Optimizing 3D Bioprinting with Machine Learning: A Simulation-Based Approach for Scaffold Design and Material Selection

3D bioprinting is an emerging novel technology in the field of tissue engineering, as it allows for the creation of complex biological structures for application in medical treatments. However, process optimization is really tricky due to factors such as scaffold design, material properties, and printing parameters. This paper covers the incorporation of machine learning to optimize 3D bioprinting, with a particular focus on scaffold design and material selection being some of the main targets for improving efficiency in bioprinting and ensuring cell viability. It uses sets of image data to enable ML models to predict conditions that are most likely to be optimal for printing. This research paper deals with the proposal for a strong ML model and its primary validation, using only simulations targeted at the tissue type of either cartilage or skin. Simulation provides an efficient way of assessing how the ML model performs in predicting optimum bioprinting parameters that offer mechanical strength and structural integrity. Besides that, the project holds great promise for the future through its potential impact on bioprinting optimization and biomedicine, due to its ability to minimize physical experimentation.