Advancements in ex vivo bone biomechanics: multimodal technologies and their integration with artificial intelligence

Bone biomechanics is essential for understanding how bone structure, composition, and mechanical function interact in healthy and diseased states. Conventional approaches such as mechanical testing, micro computed tomography, and histology provide valuable information but cannot fully capture the multiscale heterogeneity that governs bone strength and fragility. This review summarizes recent progress in advanced ex vivo bone biomechanical techniques that address these limitations, including synchrotron-based phase contrast imaging, digital volume correlation, three dimensional bioprinting, acoustic emission testing, and the combined use of nanoindentation with Raman spectroscopy. These methods allow detailed evaluation of bone microarchitecture, mineral organization, local mechanical behavior, and biochemical composition, thereby improving the detection of micro damage and early structural deterioration. The review also highlights the growing role of artificial intelligence in bone biomechanics. Machine learning approaches enhance the interpretation of complex imaging and mechanical datasets, improve the prediction of mechanical properties, and support more accurate evaluations of fracture risk. By integrating high resolution experimental techniques with data driven computational models, researchers can achieve a more complete understanding of bone quality. Together, these developments provide a focused framework for studying bone fragility and for guiding individualized diagnostic and therapeutic strategies in conditions such as osteoporosis and diabetes related bone disease.