TY  - GEN
AB  - 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.
AD  - Pacific Northwest National Laboratory
AD  - Pacific Northwest National Laboratory
AD  - Pacific Northwest National Laboratory
AD  - Pacific Northwest National Laboratory
AD  - Pacific Northwest National Laboratory
AD  - Pacific Northwest National Laboratory
AD  - Pacific Northwest National Laboratory
AD  - Pacific Northwest National Laboratory
AU  - Pope, Jenna
AU  - Sivaraman, Chitra
AU  - Ramirez, Edgar F.
AU  - Brandi-Lozano, Juan
AU  - Short, Joshua
AU  - Lewis, Isaac D.
AU  - Barnes, Brian D.
AU  - Zirkle, Lewis G.
DA  - 2020
DO  - 10.6083/5d86p095n
DO  - DOI
ID  - 8309
KW  - Orthopedics
KW  - Machine Learning
KW  - Deep Learning
KW  - Fractures, Bone
KW  - Artificial Intelligence
KW  - Radiography
KW  - Data Science
KW  - surgery
KW  - sign's online surgical database
KW  - object detection
KW  - computer vision
L1  - https://digitalcollections.ohsu.edu/record/8309/files/ResearchWeek.2020.Pope.Jenna_Abstract.pdf
L2  - https://digitalcollections.ohsu.edu/record/8309/files/ResearchWeek.2020.Pope.Jenna_Abstract.pdf
L4  - https://digitalcollections.ohsu.edu/record/8309/files/ResearchWeek.2020.Pope.Jenna_Abstract.pdf
LA  - eng
LK  - https://digitalcollections.ohsu.edu/record/8309/files/ResearchWeek.2020.Pope.Jenna_Abstract.pdf
N2  - 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.
PB  - Oregon Health and Science University
PY  - 2020
T1  - Improving radiograph analysis throughput using object detection
TI  - Improving radiograph analysis throughput using object detection
UR  - https://digitalcollections.ohsu.edu/record/8309/files/ResearchWeek.2020.Pope.Jenna_Abstract.pdf
Y1  - 2020
ER  -