Population-scale modeling of the natural knee: automated segmentation, finite element simulation, and machine learning prediction of time-series joint mechanics.

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Abstract

This study combined automated medical image segmentation, hexahedral meshing, and dynamic finite element stance-phase gait simulations with statistical shape modeling and supervised learning for 483 natural knees from the Osteoarthritis Initiative. Ridge regression and recurrent neural networks were trained to predict 38 time-series kinematic, loading, contact, and soft-tissue outputs from anatomic features. The best model, a bidirectional LSTM, achieved an average 1σ-normalized RMSE of 0.45, with inference in seconds. This population-scale framework enables rapid, subject-specific estimates of knee mechanics from imaging alone, supporting future clinical translation.

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