@article{IR,
      author = {Pope, Jenna and Sivaraman, Chitra and Ramirez, Edgar F.  and Brandi-Lozano, Juan and Short, Joshua and Lewis, Isaac  D. and Barnes, Brian D. and Zirkle, Lewis G.},
      url = {http://digitalcollections.ohsu.edu/record/8309},
      title = {Improving radiograph analysis throughput using object  detection},
      publisher = {Oregon Health and Science University},
      abstract = {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.},
      number = {IR},
      doi = {https://doi.org/10.6083/5d86p095n},
      recid = {8309},
      address = {2020},
}