Machine learning-optimized nanocomposite bone cement for enhanced postural reduction in elderly PKP surgery

Percutaneous kyphoplasty (PKP) is a popular procedure in treatment of vertebral compression fractures in the elderly, but existing bone cements are prone to poor mechanical integration, poor postural restoration, and cement leakage or poor load transfer. This paper presents a machine learning-optimized nanocomposite bone cement intended to increase biomechanical performance and achieve better postural reduction outcomes in the elderly PKP surgery. The proposed system should enhance compressive strength, modulation of elasticity, and restoration of vertebral height by incorporating nanoscale reinforcement agents into polymethyl methacrylate (PMMA)-based cement and modulating the composition parameters with the help of data-driven learning algorithms. The model takes advantage of predictive learning to determine the best nanofiller concentration, dispersion properties of the particles, and curing dynamics. The findings demonstrate that the optimized nanocomposite formulation has the potential of significantly improving structural stability and minimizing the progression of kyphotic deformity in the case of conventional cement systems. The study shows how nanomaterials engineering with machine learning can be used to improve the outcomes of minimally invasive spinal surgery.