Nov 6, 2018

Pairwise learning of MRI scans using a convolutional Siamese network for prediction of knee pain

BioRxiv : the Preprint Server for Biology
Gary H ChangVijaya B Kolachalama


Objectives: It remains difficult to characterize the source of pain in knee joints either using radiographs or magnetic resonance imaging (MRI). We sought to determine if advanced machine learning methods such as deep neural networks could distinguish knees with pain from those without it and identify the structural features that are associated with knee pain. Methods: We constructed a convolutional Siamese network to associate MRI scans obtained on subjects from the Osteoarthritis Initiative (OAI) with frequent unilateral knee pain (n=1,529) comparing the knee with frequent pain to the contralateral knee without pain. The Siamese network architecture enabled pairwise learning of information from two-dimensional (2D) sagittal intermediate-weighted turbo spin echo slices obtained from similar locations on both knees. Class activation mapping (CAM) was utilized to create saliency maps, which highlighted the regions most associated with knee pain. The MRI scans and the CAMs of each subject were reviewed by an expert radiologist to identify the presence of abnormalities within the model predicted regions of high association. Results: Using 10-fold cross validation, our model achieved an area under curve (AUC) value of 0.808. When i...Continue Reading

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Mentioned in this Paper

Biological Neural Networks
Degenerative Polyarthritis
Magnetic Resonance Imaging
Weighing Patient
Cross Validation

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