We introduce a fully automated, open-source tool for staging knee osteoarthritis severity from X-ray images.
Recent developments in machine learning, specifically in the area of deep learning, are transforming medical image analysis, yet automated analysis of X-ray and Magnetic Resonance Imaging data remains a major bottleneck in OA research. The Osteoarthritis Initiative (OAI) database presents a unique opportunity to transfer advances in deep learning to osteoarthritis research. Toward that goal, we demonstrate the feasibility of deep learning approaches to automate staging of knee OA severity from X-ray data.
You can run our software using docker software. Follow the instruction on our official github repository https://github.com/stanfordnmbl/kneenet-docker
Cite: Thomas, Kevin A., Łukasz Kidziński, Eni Halilaj, Scott L. Fleming, Guhan R. Venkataraman, Edwin HG Oei, Garry E. Gold, and Scott L. Delp. "Automated classification of radiographic knee osteoarthritis severity using deep neural networks." Radiology. Artificial intelligence 2, no. 2 (2020).