Deep Learning in Cardiovascular Tissue Engineering: A Review on Current Advances and Future Perspectives

The development of cardiovascular tissue engineering is a promising area of study in regenerative medicine, offering innovative solutions for restoring damaged cardiac structures. However, traditional methods face multiple limitations, including the complexity of scaffolds, optimal recellularization, and functional tissue maturation. At the same time, deep learning has demonstrated significant potential in biomedicine and is increasingly being explored to optimize processes. This review examines recent benefits in cardiovascular tissue engineering and the applicability of deep learning in this domain, highlighting the benefits of artificial intelligence (AI) algorithms in scaffold modeling, cellular interaction analysis, and tissue regeneration prediction. Additionally, we discuss major challenges in integrating AI, such as the lack of large, standardized datasets; the need for interpretable models for clinical use; and ethical and regulatory constraints. Despite these limitations, recent progress in AI and the availability of advanced machine learning techniques provide promising perspectives for transforming regenerative medicine. Future research should focus on improving access to relevant data, developing explainable AI models, and integrating these technologies into personalized medicine, ultimately accelerating the progression of cardiovascular tissue engineering from an experimental stage to clinical utilization.