Pain pressure threshold knee compartments10/30/2023 However, since the forces that stress the tibiofemoral contact surface generally cannot be measured in vivo, the knee adduction moment (KAM) is often used as a surrogate of medial compartment knee loading. Osteoarthritis presents more often in the medial compartment than the lateral compartment of the knee partially because the common varus (bow-leg) alignment increases medial knee contact force. Although the etiology of the disease is multifactorial, disease progression is known to be exacerbated by high joint loading during ambulation. Pain is managed pharmaceutically, while structurally cartilage is left to degrade until joint failure, at which point joint replacement surgery is recommended. Globally, one in five individuals aged 40 and older are afflicted by knee osteoarthritis, a painful joint disease that still lacks a cure or disease-modifying intervention. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.Ĭompeting interests: The authors have declared that no competing interests exist. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. įunding: NR is supported by the United States National Science Foundation Graduate Research Fellowship Program under grant numbers DGE1745016 and DGE2140739 ( ). This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All the data, code, and trained models used to produce the results presented in this manuscript are publicly available at. Received: SeptemAccepted: ApPublished: May 16, 2022Ĭopyright: © 2022 Rokhmanova et al. Dingwell, Pennsylvania State University University Park: Penn State, UNITED STATES PLoS Comput Biol 18(5):Įditor: Jonathan B. ![]() This work demonstrates the feasibility of training predictive models with synthetic data and provides clinicians with a new tool to predict the outcome of patient-specific gait retraining without requiring gait lab instrumentation.Ĭitation: Rokhmanova N, Kuchenbecker KJ, Shull PB, Ferber R, Halilaj E (2022) Predicting knee adduction moment response to gait retraining with minimal clinical data. This error is smaller than the standard deviation of the first peak KAM during baseline walking averaged across test subjects (0.306%BW*HT). On a test set of data collected by a separate research group (N = 15), the first peak KAM reduction was predicted with a mean absolute error of 0.134% body weight * height (%BW*HT). Insights learned from a ground-truth dataset with both baseline and toe-in gait trials (N = 12) enabled the creation of a large (N = 138) synthetic dataset for training the predictive model. Given the lack of large public datasets that contain different gaits for the same patient, we generated this dataset synthetically. For such a model to generalize, the training data must be large and variable. ![]() We present a regression model that uses minimal clinical data-a set of six features easily obtained in the clinic-to predict the extent of first peak KAM reduction after toe-in gait retraining. Moreover, customized interventions are time-consuming and require instrumentation not commonly available in the clinic. Although changes to the foot progression angle are overall beneficial, KAM reductions are not consistent across patients. Foot progression angle modifications that reduce the knee adduction moment (KAM), a surrogate of knee loading, have demonstrated efficacy in alleviating pain and improving function. Knee osteoarthritis is a progressive disease mediated by high joint loads.
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