Identification of Pain-Associated Effusion-Synovitis from Knee Magnetic Resonance Imaging by Deep Generative Networks
ObjectivesTo identify the source and location of osteoarthritis-induced pain symptoms, we used deep learning techniques to identify imaging abnormalities associated with pain from magnetic resonance imaging (MRI) of knees with symptoms of symptoms of osteoarthritis pain. MethodsPain-associated areas were detected from the difference between the MRI images of symptomatic knees and their respective counterfactual asymptomatic images generated by a Generative adversarial network. A total of 2,225 pairs of 3D MRI images were extracted from patients with unilateral pain symptoms in the baseline and follow-up cohorts of the Osteoarthritis Initiative. Subsequently, pain-associated effusion-synovitis were characterized into subregions (patellar, central, and posterior) using an anatomical segmentation model. ResultsWe found that the volumes of pain-associated effusion-synovitis were more sensitive and reliable indicators of pain symptoms than the overall volumes in the central and posterior subregions (odds ratio [OR]:3.23 versus 1.77 in the central region, and 3.18 versus 2.66 in the posterior region for severe effusion-synovitis). For mild effusion-synovitis, only pain-associated volume was found to be associated with pain symptoms, but not with overall volume. Patients with significant pain-associated effusion-synovitis in the patellar subregion had the highest increased odds of pain symptoms (OR=4.86). ConclusionTo the best of our knowledge, this is the first study to utilize deep-learning-based models for the detection and characterization of pain-associated imaging abnormalities. The developed algorithm can help identifying the source and location of pain symptoms and in designing targeted and individualized treatment regimens.