Keywords: Osteoarthritis, Machine Learning/Artificial Intelligence
MRI-based statistical shape models can predict future disease and distinguish between patient groups. However, these models require thousands of matching points between bones which may introduce biases and their strictly linearly orthogonal features is a limitation. This study built continuous 3D shape representations of the femur using neural implicit representations and used the learned latent space to predict knee pain. The neural shape model can generate arbitrarily high resolution surfaces and predict pain with area under the receiver operating characteristic curve of 0.7 and sensitivity of 0.89, metrics comparable to deep learning methods trained on orders of magnitude more data.
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