Pairwise learning of MRI scans using a convolutional Siamese network for prediction of knee pain
bioRxiv, 2018•biorxiv.org
Background and objective It remains difficult to characterize pain in knee joints with
osteoarthritis solely by radiographic findings. We sought to understand how advanced
machine learning methods such as deep neural networks can be used to analyze raw MRI
scans and predict bilateral knee pain, independent of other risk factors. Methods We
developed a deep learning framework to associate information from MRI slices taken from
the left and right knees of subjects from the Osteoarthritis Initiative with bilateral knee pain …
osteoarthritis solely by radiographic findings. We sought to understand how advanced
machine learning methods such as deep neural networks can be used to analyze raw MRI
scans and predict bilateral knee pain, independent of other risk factors. Methods We
developed a deep learning framework to associate information from MRI slices taken from
the left and right knees of subjects from the Osteoarthritis Initiative with bilateral knee pain …
Background and objective
It remains difficult to characterize pain in knee joints with osteoarthritis solely by radiographic findings. We sought to understand how advanced machine learning methods such as deep neural networks can be used to analyze raw MRI scans and predict bilateral knee pain, independent of other risk factors.
Methods
We developed a deep learning framework to associate information from MRI slices taken from the left and right knees of subjects from the Osteoarthritis Initiative with bilateral knee pain. Model training was performed by first extracting features from two-dimensional (2D) sagittal intermediate-weighted turbo spin echo slices. The extracted features from all the 2D slices were subsequently combined to directly associate using a fused deep neural network with the output of interest as a binary classification problem.
Results
The deep learning model resulted in predicting bilateral knee pain on test data with 70.1% mean accuracy, 51.3% mean sensitivity, and 81.6% mean specificity. Systematic analysis of the predictions on the test data revealed that the model performance was consistent across subjects of different Kellgren-Lawrence grades.
Conclusion
The study demonstrates a proof of principle that a machine learning approach can be applied to associate MR images with bilateral knee pain.
SIGNIFICANCE AND INNOVATION
Knee pain is typically considered as an early indicator of osteoarthritis (OA) risk. Emerging evidence suggests that MRI changes are linked to pre-clinical OA, thus underscoring the need for building image-based models to predict knee pain. We leveraged a state-of-the-art machine learning approach to associate raw MR images with bilateral knee pain, independent of other risk factors.
biorxiv.org
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