As the use of car dashboard cameras (dashcams) has increased, the availability of dashcam imagery has also increased. In recent years, dashcam imagery has been predominantly used in conjunction with computer vision techniques for autonomous vehicle systems. However, this research explores an alternative application of these technologies in the domain of public safety and security. Specifically, we apply object detection to dashcam imagery to address the challenge of identifying vehicles associated with active Amber Alerts. With the goal of aiding law enforcement in locating abducted children more efficiently, we employ the YOLO (You Only Look Once) object detection model, a state-of-the-art deep learning framework known for its real-time performance and accuracy. Our methodology involves training and fine-tuning the YOLO model on a custom dataset of dashcam footage, incorporating diverse environmental conditions such as varying lighting, weather, and traffic scenarios. Experimental results demonstrate that the model achieves high precision and recall rates in detecting target vehicles, validating its effectiveness for real-world deployment. This research highlights the potential of leveraging deep learning and computer vision techniques to address critical public safety challenges, offering a novel application of these technologies beyond their traditional use in autonomous driving. Our findings contribute to the growing body of work in computer science that seeks to harness AI for societal benefit.
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.
