Hirvasniemi, J., Runhaar, J., van der Heijden, R. A., Zokaeinikoo, M., Yang, M., Li, X., Tan, J., Rajamohan, H. R., Zhou, Y., Deniz, C. M., Caliva, F., Iriondo, C., Lee, J. J., Liu, F., Martinez, A. M., Namiri, N., Pedoia, V., Panfilov, E., Bayramoglu, N., … Klein, S. (2023). The KNee OsteoArthritis Prediction (Knoap2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images. Osteoarthritis and Cartilage, 31(1), 115–125. https://doi.org/10.1016/j.joca.2022.10.001
The KNee OsteoArthritis prediction (KNOAP2020) challenge : an image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images
|Author:||Hirvasniemi, J.1; Runhaar, J.2; van der Heijden, R.A.1;|
1Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
2Department of General Practice, Erasmus MC University Medical Center, Rotterdam, the Netherlands
3Department of Biomedical Engineering, Cleveland Clinic, Cleveland, USA
4Department of Radiology, New York University Langone Health, New York, USA
5Department of Radiology, University of California, San Francisco, San Francisco, USA
6Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
7Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
8Akousist Co., Ltd., Taoyuan City, Taiwan
9Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
10Department of Radiology, Stanford University, Stanford, USA
11Department of Orthopedics & Sport Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
|Online Access:||PDF Full Text (PDF, 0.9 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022102462777
|Publish Date:|| 2022-10-24
Objectives: The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth.
Design: The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC).
Results: Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57–0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52–0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables.
Conclusions: The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.
Osteoarthritis and cartilage
|Pages:||115 - 125|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
217 Medical engineering
The PROOF study was funded by ZonMw, the Netherlands Organisation for Health Research and Development (Grant number: 120520001). Study supported in part by National Institutes of Health. The funding sources had no role in the study design, data collection or analysis, interpretation of data, writing of the manuscript, or in the decision to submit the manuscript for publication.
© 2022 The Author(s). Published by Elsevier Ltd on behalf of Osteoarthritis Research Society International. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).