Recurrence Dynamics of Extravasation on Computed Tomography

The discovery of complex patterns in extravasation on computed tomography (CT) imaging is crucial for improving the management of trauma patients. These patterns can aid in identifying complications such as vascular injury, organ rupture, or active hemorrhage, enabling timely and targeted interventions to improve patient outcomes. This study explores fuzzy logic-based nonlinear dynamic features extracted from CT imaging of extravasation, aiming to uncover radiographic patterns in trauma cases to support clinical prediction and intervention. The temporal and spatial evolution of extravasation imaging in three trauma cases are analyzed. Using geostatistics and nonlinear dynamics, this study aims to identify subtle, dynamic patterns that can enhance real-time detection and prediction of complications. These findings offer a foundation for integrating advanced imaging analytics into trauma care, improving decision-making, and optimizing patient outcomes through early intervention.