Machine learning-assisted stiffness prediction in high-cell-density bioprinting

This study developed a neural network-based machine learning model to predict the stiffness of high-cell-density (HCD) bioprinted constructs produced by digital light processing (DLP) 3D printing. The approach addressed the challenge of stiffness modulation in cell-laden GelMA scaffolds, where cell density alters optical scattering and crosslinking efficiency. Using experimental mechanical testing data, the authors trained and validated a transfer learning neural network capable of predicting scaffold stiffness (0.2–6 kPa range) from cell density and light exposure parameters. The model achieved a mean absolute error below 300 Pa and accurately adapted to new cell types (e.g., HepG2) with limited data, enabling efficient stiffness tuning in DLP bioprinting for precision tissue engineering.