KNOAP Challenge and data management with XNAT
Published: 2024-03-11
Join us for an afternoon of presentations from Euro-BioImaging users and image data/image analysis experts at our Nodes that will provide a compelling overview of the state-of-the-art in Image Data. At the Euro-BioImaging User Forum on Image Data, Jukka Hirvasniemi, Erasmus MC, Population Imaging Flagship Node, presents the KNee OsteoArthritis Prediction (KNOAP) challenge and how it made it possible to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis.
What: Euro-BioImaging User Forum “Image Data”
When: March 26, 2024, from 14:00-17:00 CEST Where: Online
Abstract
KNOAP Challenge and data management with XNAT
Jukka Hirvasniemi,
Erasmus MC, Population Imaging Flagship Node
Osteoarthritis is the most common joint disease which affects over 250 million people worldwide. Identification of subjects at high risk to develop knee osteoarthritis is important to prevent or slow down the disease process. The KNee OsteoArthritis Prediction (KNOAP) challenge (https://knoap2020.grand-challenge.org/) was organised to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth. 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 through XNAT. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis within 78 months, were not provided to the participants. The model with the highest area under the receiver operating characteristic curve (0.64) used deep learning to extract information from X-ray images combined with clinical variables. The KNOAP 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.
