Application of Machine Learning in Predicting Osteogenic Differentiation of Mesenchymal Stem Cells

This article reviews the progress made in applying machine learning to predict the osteogenic differentiation of mesenchymal stem cells. Bone defects pose a significant clinical challenge due to limitations of traditional therapies such as autologous bone graft donor shortages, allograft immune risks and so on. Mesenchymal stem cells offer a promising solution for bone regeneration due to their osteogenic differentiation potential, but their clinical utility is hindered by unpredictable differentiation efficiency and heterogeneity. Machine learning has emerged as a powerful tool to address these issues by enabling early, non-invasive prediction of osteogenic differentiation and high-throughput analysis of complex data like morphology and omics. This review systematically summarizes the application of ML in three key areas: early prediction using cellular morphology, omics data analysis for biomarker discovery, and drug/biomaterial screening for enhancing osteogenesis. We compare the performance of multiple ML models like ResNet-50, LASSO, and random forests and highlight their advantages and limitations. Additionally, we discuss challenges in data standardization and model interpretability, and propose future directions for translating ML into clinical practice. This review provides a comprehensive overview of how ML can revolutionize MSC-based bone regeneration by improving prediction accuracy and optimizing therapeutic strategies.