The KNee OsteoArthritis Prediction (KNOAP2020) challenge:An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images

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.</p

Kinetic, Isotherm and Thermodynamic Analysis on Adsorption of Cr(VI) Ions from Aqueous Solutions by Synthesis and Characterization of Magnetic-Poly(divinylbenzene-vinylimidazole) Microbeads

The magnetic-poly(divinylbenzene-1-vinylimidazole) [m-poly(DVB-VIM)] microbeads (average diameter 53–212 μm) were synthesized and characterized; their use as adsorbent in removal of Cr(VI) ions from aqueous solutions was investigated. The m-poly(DVB-VIM) microbeads were prepared by copolymerizing of divinylbenzene (DVB) with 1-vinylimidazole (VIM). The m-poly(DVB-VIM) microbeads were characterized by N2 adsorption/desorption isotherms, ESR, elemental analysis, scanning electron microscope (SEM) and swelling studies. At fixed solid/solution ratio the various factors affecting adsorption of Cr(VI) ions from aqueous solutions such as pH, initial concentration, contact time and temperature were analyzed. Langmuir, Freundlich and Dubinin–Radushkvich isotherms were used as the model adsorption equilibrium data. Langmuir isotherm model was the most adequate. The pseudo-first-order, pseudo-second-order, Ritch-second-order and intraparticle diffusion models were used to describe the adsorption kinetics. The apparent activation energy was found to be 5.024 kJ mol−1, which is characteristic of a chemically controlled reaction. The experimental data fitted to pseudo-second-order kinetic. The study of temperature effect was quantified by calculating various thermodynamic parameters such as Gibbs free energy, enthalpy and entropy changes. The thermodynamic parameters obtained indicated the endothermic nature of adsorption of Cr(VI) ions. Morever, after the use in adsorption, the m-poly(DVB-VIM) microbeads with paramagnetic property were separeted via the applied magnetic force. The magnetic beads could be desorbed up to about 97% by treating with 1.0 M NaOH. These features make the m-poly(DVB-VIM) microbeads a potential candidate for support of Cr(VI) ions removal under magnetic field