Innovative Orthopedic Solutions for AI-Optimized Piezoelectric Implants for Superior Patient Care

Abstract

This research aims to optimize piezoelectric implants for orthopedic applications, enhancing energy harvesting efficiency and mechanical integrity. Our objectives include comparing piezoelectric materials (PZT, PVDF, and BaTiO3) and employing advanced theoretical modeling, finite element analysis (FEA), and validation to identify optimal configurations. Methodologically, this study integrates machine learning and AI-driven techniques to refine design parameters and predict performance outcomes. Significant findings have revealed that PZT demonstrated the highest sensitivity (2 V/mm), achieving a maximum power output of 4.10 Watts, surpassing traditional solutions by over 100%. The optimization process ensured uniform stress distribution, reducing mechanical failure risk, with predictive models showing high accuracy (R-squared value of 97.77%). Error analysis indicated minimal discrepancies, with an average error margin of less than 2%. The conclusions highlight the significant potential of optimized piezoelectric implants in developing durable, efficient, and patient-friendly orthopedic solutions, setting a new standard in intelligent medical device innovation and contributing to enhanced patient care and improved clinical outcomes.