Application of machine learning in the field of porous bioceramics

Bioceramics have garnered significant interest within biomedical engineering due to their unique combination of biocompatibility, bioactivity, and tunable porosity, making them ideal candidates for bone grafts, implants, and tissue scaffolds. However, the inherent challenge of balancing mechanical integrity with biological performance often complicates their design and fabrication. As such, this chapter examines how machine learning (ML) methodologies are reshaping the landscape of bioceramic research with focus on porous structures. By harnessing large datasets from experimental studies, microstructural imaging, and computational modeling, ML offers a data-driven framework capable of identifying complex, nonlinear relationships that govern scaffold behavior. Techniques, such as convolutional neural networks, generative adversarial networks, and reinforcement learning, are highlighted for their role in predicting mechanical properties, optimizing pore structures, and improving the reproducibility of additive manufacturing processes. The chapter further explores the integration of ML with real-time process monitoring and patient-specific data to support the development of personalized implants. Finally, critical challenges, such as the scarcity of standardized datasets, limited model interpretability, and the need for interdisciplinary collaboration, are discussed alongside emerging, tangible solutions. By bridging computational modeling and experimental validation, ML-driven approaches hold the potential to accelerate the development of the next generation of bioceramics, tailored for clinical success and manufacturing feasibility.