Advancing scaffold porosity through a machine learning framework in extrusion based 3D bioprinting - Limon, Shah M.; Quigley, Connor; Sarah, Rokeya; Habib, Ahasan (Frontiers in Materials, 2024)
Three Dimensional (3D) bioprinting holds great promise for tissue and organ regeneration due to its inherent capability to deposit biocompatible materials containing live cells in precise locations. Extrusion-based 3D bioprinting (EBP) method stands out for its ability to achieve a higher cell release rate, ensuring both external and internal scaffold structures. The systematic adjustment of key process parameters of EBP, including nozzle diameter, printing speed, print distance, extrusion pressure, material fraction, and viscosity allows for precise control over filament dimensions, ultimately shaping the desired scaffold porosity as per user specifications. However, managing these factors with all possible interactions simultaneously to achieve the desired filament width can be intricate and resource intensive. This study presents a novel framework designed to construct a predictive model for the filament width of 3D bioprinted scaffolds for various process parameters. A total of 157 experiments have been conducted under various combinations of process parameters and biomaterial's weight fraction for this study purpose. A regression-based machine learning approach is employed to develop the predictive model utilizing Adj. R 2 , Mallow's Cp, and Bayesian Information Criterion (BIC). Following model development, rigorous experimental validations are conducted to assess the accuracy and reliability of the model. Based on the cross-validation of randomly split test data, Adj. R 2 model emerges as the highest performing machine learning model (Mean Squared Error, MSE = 0.0816) compared to Mallow's Cp and BIC (MSE = 0.0841 and 0.0877, respectively) models. The comparative analysis results between the experimental and model's data demonstrate that our predictive model achieves an accuracy of approximately 85% in filament width prediction. This framework presents a significant advancement in the precise control and optimization of 3D bioprinted scaffold fabrication, offering valuable insights for the advancement of tissue engineering and regenerative medicine applications.