سامانه همایش های دانشگاه آزاد اسلامی واحد نجف آباد

صفحه اصلی / International Conference on Artificial Intelligence; City, Industry and Health

AI-Powered Optimization of Magnesium Phosphate Bone Cements for Patient-Specific Orthopedic Outcomes

نویسندگان :

Mehran Shafiei¹ Mohamad Shahgholi²

1- Department of Mechanical Engineering, Na.C., Islamic Azad University, Najafabad, Iran 2- Department of Mechanical Engineering, Na.C., Islamic Azad University, Najafabad, Iran

کلمات کلیدی :

Artificial Intelligence،Machine Learning،Magnesium Phosphate Bone Cements،Patient-Specific Design،Personalized Orthopedics

چکیده :

Magnesium phosphate bone cements (MPCs) have adjustable properties like compressive strength, setting time, degradation rate, cytotoxicity, and bone-forming ability, thereby making them useful products in orthopedic sciences dealing with joint replacements and fracture fixation. As with most standard formulations, this fails to meet individual patient needs and thereby contributes to failure rates between 10% and 15%. This article presents an artificial intelligence (AI)-driven framework to optimize the compositions of MPCs by integrating a theoretical database of property variants with patient-specific data covering age, sex, disease history, and different biomechanical considerations. In principle, the deep neural network (DNN) is chosen for its superior ability to deal with the complexity of relationships with multiple outputs. Furthermore, cement design can be such that it benefits from reduced chances of failure, fewer revisions, and increased bone regeneration. Optimization strategies, GPU-accelerated computing and Bayesian hyperparameter tuning, are outlined for the scalability and accurate implementation of such a framework. This would not only capitalize on AI-supported robotic surgery and personalized treatment plans, but it is, more importantly, promising future prospects for global healthcare like in Iran, which would be ready to consume such high demand in orthopedic requirements.