Prediction of Knee Joint Compartmental Loading Maxima Utilizing Simple Subject Characteristics and Neural Networks.

ABSTRACT: Joint loading may affect the development of osteoarthritis, but patient-specific load estimation requires cumbersome motion laboratory equipment. This reliance could be eliminated using artificial neural networks (ANNs) to predict loading from simple input predictors. We used subject-specific musculoskeletal simulations to estimate knee joint contact forces for 290 subjects during over 5000 stance phases of walking and then extracted compartmental and total joint loading maxima from the first and second peaks of the stance phase. We then trained ANN models to predict the loading maxima from predictors that can be measured without motion laboratory equipment (subject mass, height, age, gender, knee abduction-adduction angle, and walking speed). When compared to the target data, our trained

SUBMITTER: Lavikainen J 

PROVIDER: S-EPMC10598099 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature