Imaging-based tracking of stem-cell responses for 3D bioprinting optimization.

Abstract

This review examines how mechanical and chemical stimuli shape stem-cell behavior and how live-cell imaging combined with artificial intelligence can support the evaluation and optimization of 3D bioprinting workflows. Current evidence indicates that substrate stiffness, stretch, shear stress, compression, and soluble factors such as transforming growth factor beta (TGF-β), bone morphogenetic protein-2 (BMP-2), vascular endothelial growth factor (VEGF), and basic fibroblast growth factor (FGF-2) influence viability, migration, morphology, and lineage commitment in biopolymeric constructs. Imaging modalities including phase-contrast, fluorescence, confocal, two-photon, and light-sheet microscopy enable dynamic observation of these responses, while computational pipelines for segmentation, tracking, and feature extraction improve scalability and reproducibility of analysis. Across the reviewed studies, image-derived readouts such as cell distribution, motility, adhesion patterns, and early morphological changes emerge as promising indicators of construct quality and maturation potential. At the same time, broader application remains limited by phototoxicity, imaging depth constraints, data-processing demands, incomplete standardization of metrics, and restricted model generalizability. Overall, imaging-based cell tracking integrated with AI-assisted analysis offers a practical framework for post-print evaluation and iterative optimization in 3D bioprinting, with potential to strengthen quality control, reproducibility, and biological interpretation in regenerative engineering.