Assessment of knee pain from MR imaging using a convolutional Siamese network

ConclusionsThis study demonstrates a proof of principle that deep learning can be applied to assess knee pain from MRI scans.Key Points• Our article is the first to leverage a deep learning framework to associate MR images of the knee with knee pain.• We developed a convolutional Siamese network that had the ability to fuse information from multiple two-dimensional (2D) MRI slices from the knee with pain and the contralateral knee of the same individual without pain to predict unilateral knee pain.• Our model achieved an area under curve (AUC) value of 0.808. When individuals who had WOMAC pain scores that were not discordant for knees (pain discordance <  3) were excluded, model performance increased to 0.853.
Source: European Radiology - Category: Radiology Source Type: research