The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images
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The KNee OsteoArthritis Prediction (KNOAP2020) challenge : An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images. / 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.; Nguyen, H. H.; Nieminen, M. T.; Saarakkala, S.; Tiulpin, A.; Lin, E.; Li, A.; Li, V.; Dam, E. B.; Chaudhari, A. S.; Kijowski, R.; Bierma-Zeinstra, S.; Oei, E. H.G.; Klein, S.
In: Osteoarthritis and Cartilage, Vol. 31, 2023, p. 115-125.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - The KNee OsteoArthritis Prediction (KNOAP2020) challenge
T2 - An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images
AU - Hirvasniemi, J.
AU - Runhaar, J.
AU - van der Heijden, R. A.
AU - Zokaeinikoo, M.
AU - Yang, M.
AU - Li, X.
AU - Tan, J.
AU - Rajamohan, H. R.
AU - Zhou, Y.
AU - Deniz, C. M.
AU - Caliva, F.
AU - Iriondo, C.
AU - Lee, J. J.
AU - Liu, F.
AU - Martinez, A. M.
AU - Namiri, N.
AU - Pedoia, V.
AU - Panfilov, E.
AU - Bayramoglu, N.
AU - Nguyen, H. H.
AU - Nieminen, M. T.
AU - Saarakkala, S.
AU - Tiulpin, A.
AU - Lin, E.
AU - Li, A.
AU - Li, V.
AU - Dam, E. B.
AU - Chaudhari, A. S.
AU - Kijowski, R.
AU - Bierma-Zeinstra, S.
AU - Oei, E. H.G.
AU - Klein, S.
N1 - Publisher Copyright: © 2022 The Author(s)
PY - 2023
Y1 - 2023
N2 - 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. Conclusion: 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.
AB - 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. Conclusion: 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.
KW - Deep learning
KW - Knee osteoarthritis
KW - Machine learning
KW - Magnetic resonance imaging
KW - Prediction
KW - Radiography
UR - http://www.scopus.com/inward/record.url?scp=85141304643&partnerID=8YFLogxK
U2 - 10.1016/j.joca.2022.10.001
DO - 10.1016/j.joca.2022.10.001
M3 - Journal article
C2 - 36243308
AN - SCOPUS:85141304643
VL - 31
SP - 115
EP - 125
JO - Osteoarthritis and Cartilage
JF - Osteoarthritis and Cartilage
SN - 1063-4584
ER -
ID: 326676724