Artificial Intelligence (AI) agents are transforming healthcare by automating tasks, enhancing diagnostic precision, and enabling personalized care. Our project aims to develop an AI-based system to automate the detection of IVC filters and complications, such as extravascular extension, in CT scans. IVC filters are crucial for patients with venous blood clots but are meant to be temporary, and delays in their removal can cause harm. Interventional radiology (IR) practices often rely on manual tracking methods, which are inadequate when patients transfer care. Many patients forget their filter’s presence, leaving new providers unaware. Building on previous research with Mayo Clinic NWWI, we aim to enhance an existing deep learning algorithm for IVC flagging and extend it to detect extravascular extension, flagging patients for closer follow-up. The system will also integrate large language models (LLMs) to process electronic health records (EHRs) and be modular for future expansion. Our goal is to create a reliable AI algorithm for detecting IVC filters and implement it in hospital settings.
