A neural network to predict the knee adduction moment in patients with osteoarthritis using anatomical landmarks obtainable from 2D video analysis
| Autor: | Scott L. Delp, Scott D. Uhlrich, Gary S. Beaupre, Julie A Kolesar, Garry E. Gold, Łukasz Kidziński, Kevin A. Thomas, M.A. Boswell |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Adult Male Computer science Clinical Decision-Making Biomedical Engineering Video Recording Motion capture Article Inverse dynamics 03 medical and health sciences Young Adult 0302 clinical medicine Gait (human) Rheumatology medicine Humans Orthopedics and Sports Medicine Aged 030203 arthritis & rheumatology Artificial neural network business.industry Pattern recognition Middle Aged Osteoarthritis Knee Sagittal plane Biomechanical Phenomena 030104 developmental biology medicine.anatomical_structure Gait analysis Coronal plane Test set Case-Control Studies Feasibility Studies Female Artificial intelligence Neural Networks Computer Anatomic Landmarks business Gait Analysis |
| Zdroj: | Osteoarthritis Cartilage |
| ISSN: | 1522-9653 |
| Popis: | Summary Objective The knee adduction moment (KAM) can inform treatment of medial knee osteoarthritis; however, measuring the KAM requires an expensive gait analysis laboratory. We evaluated the feasibility of predicting the peak KAM during natural and modified walking patterns using the positions of anatomical landmarks that could be identified from video analysis. Method Using inverse dynamics, we calculated the KAM for 86 individuals (64 with knee osteoarthritis, 22 without) walking naturally and with foot progression angle modifications. We trained a neural network to predict the peak KAM using the 3-dimensional positions of 13 anatomical landmarks measured with motion capture (3D neural network). We also trained models to predict the peak KAM using 2-dimensional subsets of the dataset to simulate 2-dimensional video analysis (frontal and sagittal plane neural networks). Model performance was evaluated on a held-out, 8-person test set that included steps from all trials. Results The 3D neural network predicted the peak KAM for all test steps with r2( Murray et al., 2012) 2 = 0.78. This model predicted individuals’ average peak KAM during natural walking with r2( Murray et al., 2012) 2 = 0.86 and classified which 15° foot progression angle modifications reduced the peak KAM with accuracy = 0.85. The frontal plane neural network predicted peak KAM with similar accuracy (r2( Murray et al., 2012) 2 = 0.85) to the 3D neural network, but the sagittal plane neural network did not (r2( Murray et al., 2012) 2 = 0.14). Conclusion Using the positions of anatomical landmarks from motion capture, a neural network accurately predicted the peak KAM during natural and modified walking. This study demonstrates the feasibility of measuring the peak KAM using positions obtainable from 2D video analysis. |
| Databáze: | OpenAIRE |
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