Keywords: Osteoarthritis, MSK, shape model, MOAKS, osteophytes
Motivation: Osteoarthritis is a whole joint disease that requires quantification, localization, and visualization of disease related features of bones and cartilage.
Goal(s): To develop a novel neural shape model (NSM) that can encode and reconstruct bone and cartilage shape, while quantifying localized features of OA.
Approach: We trained a NSM on 6,325 knees and compared its reconstructions to a conventional statistical shape model and its ability to predict localized disease to a convolutional neural network.
Results: The NSM reconstructed tissues with cartilage thickness correlations >0.993. NSM representations accurately diagnosed OA and predicted localized severity of osteophytes and cartilage defects better than a CNN.
Impact: Our NSM can reconstruct whole bone and cartilage morphology, while encoding localized pathology specific information. Research use of the NSM can unlock novel insights into OA pathophysiology. Clinical deployment would enable automated insights into whole joint health.
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