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We present a model, trained on synthetic data, to predict the extent of first peak KAM reduction after toe-in gait retraining.


About: Although foot progression angle gait retraining is overall beneficial as a conservative intervention for knee osteoarthritis, knee adduction moment (KAM) reductions are not consistent across patients. Moreover, customized gait interventions are time-consuming and require instrumentation not commonly available in the clinic. We present a model that uses minimal clinical data to predict the extent of first peak KAM reduction after toe-in gait retraining. Given the lack of large public datasets that contain different gaits for the same patient, we present a method to generate toe-in gait data synthetically, and share the resultant trained model.

Data are available under Downloads > Data Share
Code and trained models are available on GitHub: https://github.com/CMU-MBL/predictKAMreduction

Citation: Rokhmanova N, Kuchenbecker KJ, Shull PB, Ferber R, Halilaj E (2022) Predicting knee adduction moment response to gait retraining with minimal clinical data. PLoS Comput Biol 18(5): e1009500. https://doi.org/10.1371/journal.pcbi.1009500

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