Automated Multi-Modal Anomaly Detection and Predictive Maintenance for Medical Implant Longevity via HyperScore-Augmented Bayesian Optimization

**Abstract:** This paper introduces a novel framework for proactively extending the lifespan and optimizing performance of implanted medical devices. By integrating multi-modal sensor data (electrophysiological, biomechanical, electrochemical), a comprehensive anomaly detection system is built and fortified using a HyperScore-augmented Bayesian Optimization (HS-BO) approach. This ensures more efficient resource allocation for predictive maintenance, minimizing patient intervention and maximizing implant longevity. The system fundamentally departs from reactive maintenance strategies by dynamically learning device degradation patterns and predicting imminent failure events with unprecedented accuracy, leveraging established HIT technologies like sensor fusion, machine learning, and Bayesian optimization, now synergistically combined within a novel HyperScore feedback loop. This approach has the potential to revolutionize implant lifecycle management, reducing healthcare costs and improving patient outcomes, estimated to represent a $25B market within the next decade. Our rigorous methodology incorporates automated protocol rewriting, digital twin simulations, and real-time data analysis to achieve superior predictive capabilities compared to existing models.