SIGN Fracture Care International partners with surgeons in low-resource hospitals worldwide to provide access to effective orthopedic care. SIGN reaches across 52 countries and interacts with over 5,000 surgeons, but expanding their care has led to an overwhelming amount of medical data; SIGN's Online Surgical Database (SOSD) contains over 500,000 images spanning two decades and is continuing to grow. We apply machine learning tools to the SOSD to improve the throughput of radiograph analysis to assist SIGN in further expanding their reach and effectively helping surgeons and patients.
Details
Title
Improving x-ray analysis throughput using object detection
Creator
Pope, Jenna : Pacific Northwest National Laboratory Sivaraman, Chitra : Pacific Northwest National Laboratory Ramirez, Edgar F. : Pacific Northwest National Laboratory Brandi-Lozano, Juan : Pacific Northwest National Laboratory Short, Joshua : Pacific Northwest National Laboratory Lewis, Isaac D. : Pacific Northwest National Laboratory Barnes, Brian D. : Pacific Northwest National Laboratory Zirkle, Lewis G. : Pacific Northwest National Laboratory
Meeting Name
Research Week, Oregon Health and Science University, 2020
Related work citations
1. Bilbrey, J. A., Ramirez, E. F., Brandi-Lozano, J., Sivaraman, C., Short, J., Lewis, I. D., … Zirkle, L. G. (2020). Improving radiograph analysis throughput through transfer learning and object detection. AME Publishing Company. https://doi.org/10.21037/jmai-20-2 2. Pope, J. (2020). Improving Radiograph Analysis Throughput using Object Detection. OHSU. https://doi.org/10.6083/5D86P095N
