Home > Publication > Constructing a clinical radiographic knee osteoarthritis database using artificial intelligence tools with limited human labor: A proof of principle

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Constructing a clinical radiographic knee osteoarthritis database using artificial intelligence tools with limited human labor: A proof of principle

Authors:

Anders Lenskjold, Mathias W. Brejnebøl, Janus U. Nybing, Martin H. Rose, Henrik Gudbergsen, Anders Troelsen, Anne Moller, Henriette Raaschou, and Mikael Boesen

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Objective

This study looks at creating a scalable and feasible retrospective consecutive knee osteoarthritis (OA) radiographic database with limited human labor using the AI solution RBknee and 3 other AI tools.

Open access

This research is open access

This original research can be read in Osteoarthrisis and Cartilage in volume 32, issue 3, published March 2024.

Funding and conflicts of interests

This research was funded by an unrestricted Signature Project grant from the Danish Agency for Digital Government sponsored the study and the primary author’s salary. M.B. and A.T. are medical advisors for Radiobotics and have assets in the company. J.U.N. is a former member of Teal Medical’s advisory board. A.L., M.W.B., M.H.R., H.G., A.M., and H.R. have no conflict of interest. Radiobotics ApS contributed to customer support for their AI tool. The hospital department purchased the AI tool for clinical and research purposes.

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