Smart Sensor Network Architecture with Machine Learning–Based Predictive Monitoring for High-Complexity Computed Tomography Systems

This study addresses the need for intelligent condition monitoring in high-complexity medical imaging systems by proposing a smart sensing architecture for the Revolution EVO Computed Tomography (CT) scanner. Ensuring operational reliability and minimizing unexpected downtime remain critical challenges in advanced CT platforms, motivating the integration of distributed sensing and data-driven analytics. The proposed framework combines Smart Sensor Networks with Machine Learning (ML)-based analysis to enable continuous acquisition and synchronization of heterogeneous operational data from key subsystems, including the X-ray tube assembly, detector array, rotational gantry mechanism, and data acquisition and processing unit. Multivariate feature extraction and sensor-level data fusion are employed to support anomaly detection and predictive assessment of system behavior. The methodology is informed by technical documentation and system specifications provided by GE HealthCare, together with established approaches in intelligent sensing and predictive analytics. The results demonstrate that structured integration of multi-sensor data and ML-based inference can enhance diagnostic sensitivity and enable early identification of abnormal operational patterns. It is concluded that a sensor-centric monitoring architecture provides a feasible pathway toward improved reliability, reduced unplanned interruptions, and more efficient lifecycle management of CT imaging systems.