The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI …
J Hirvasniemi, J Runhaar, RA van der Heijden… - Osteoarthritis and …, 2023 - Elsevier
Osteoarthritis and cartilage, 2023•Elsevier
Summary 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 …
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 …
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.
Elsevier
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