Predicting Medical Device Life Expectancy and Estimating Remaining Useful Life Using a Data-Driven Multimodal Framework

The objective of this project is to develop a data-driven multimodal framework to predict the life expectancy of medical devices and estimate their remaining useful life (RUL). By integrating various data sources, including sensor data, usage logs, and maintenance records, the system applies machine learning and deep learning techniques to analyze the health status of medical devices. The framework aims to provide accurate predictions regarding device lifespan, enabling proactive maintenance and replacement decisions. This approach improves operational efficiency, reduces unexpected failures, and ensures optimal performance of medical devices in healthcare settings.