The delivery of medical imaging services involves significant resources, including equipment, materials and labor. Proper coordination of these resources positively impacts the productivity of the radiology department, which, in turn, influences access to care, costs, and quality. Currently, the most commonly used techniques to measure work task productivity in radiology departments are manual and have not changed in over 40 years. This research project focuses on computer vision-based approaches to capturing workflow activity data related to the delivery of medical imaging services.

The SUDS Scholar will work closely with the principal investigator and students in the lab to design and implement edge computing prototypes using devices such as the Raspberry Pi and Google Coral Dev Board to collect video data. The SUDS Scholar will use open-source frameworks such as Gstreamer for image handling and Tensorflow to apply deep learning to the image data. The data collected from these approaches will be used to extract metrics important in department performance such as cycle time, flow rate, capacity and utilization. These video-based approaches may provide a nonintrusive, easy, inexpensive, and rapid mechanism for generating operational information and knowledge on the productivity of the medical imaging department.

Join our exciting research project and become a crucial part of our mission to revolutionize healthcare delivery. As a SUDS scholar, you’ll collaborate closely with our principal investigator and research assistants (including a former SUDS scholar). Together, we’re developing cutting-edge, real-time intelligent video analytics tools that have a direct impact on hospitals and medical imaging departments. Your role will involve crafting and implementing edge computing prototypes utilizing advanced technology like the Nvidia Jetson, the world’s leading AI computing platform. You’ll harness the power of open-source frameworks such as Gstreamer and Tensorflow to apply deep learning techniques to image data, unlocking valuable insights. The data you gather will be the key to extracting essential performance metrics and enhancing hospital productivity. Notably, our previous SUDS scholar successfully deployed vision-based AI tools in our hospital’s CT suite last summer. This year, our focus is on aggregating data from multiple image sensors to gain deeper insights across diverse environments. Join us on this exciting journey, where your work will directly influence healthcare access, costs, and quality. If you’re passionate about AI, healthcare, and making a real-world impact, this project may be perfect for you!