Artificial Intelligence (AI) agents are transforming healthcare by automating tasks and improving diagnostic precision. Our project focuses on developing an AI-based system specifically to detect extravascular extension of inferior vena cava (IVC) filter struts on CT scans. Although IVC filters are intended to be temporary, prolonged dwell time increases the likelihood of strut penetration beyond the IVC wall. Extravascular extension, defined as filter struts penetrating beyond the IVC wall into surrounding structures, increases the risk of organ injury, pain, bleeding, and complex retrieval. Interventional radiology (IR) practices often rely on manual tracking systems, which are insufficient when patients transfer care or are lost to follow-up. Many patients are unaware a filter remains in place, and new providers may not recognize associated complications. Building on prior research with Mayo Clinic Health System, we aim to enhance an existing deep learning framework to localize filter struts and quantitatively assess their extension relative to the IVC boundary. After segmentation of the IVC, our model will localize filter struts relative to the vessel wall to improve complication detection. The system will also incorporate large language models (LLMs) to process electronic health records (EHRs) and support automated follow-up flagging for safer long-term patient management.
