Machine learning algorithms for predicting scapular kinematics
| Autor: | Elizabeth A. Rapp van Roden, Jim Richards, Kristen F. Nicholson, R. Garry Quinton, R. Tyler Richardson, Kert F. Anzilotti |
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| Rok vydání: | 2019 |
| Předmět: |
Adult
Computer science Movement 0206 medical engineering Biomedical Engineering Biophysics 02 engineering and technology Kinematics Machine learning computer.software_genre Motion capture Biplane Machine Learning Computer Science::Robotics 03 medical and health sciences Imaging Three-Dimensional 0302 clinical medicine medicine Humans Fluoroscopy Astrophysics::Galaxy Astrophysics Mechanical Phenomena Artificial neural network medicine.diagnostic_test Orientation (computer vision) business.industry Biomechanics 2D to 3D conversion 020601 biomedical engineering Healthy Volunteers Biomechanical Phenomena Scapula Artificial intelligence business Algorithm computer 030217 neurology & neurosurgery |
| Zdroj: | Medical Engineering & Physics. 65:39-45 |
| ISSN: | 1350-4533 |
| DOI: | 10.1016/j.medengphy.2019.01.005 |
| Popis: | The goal of this study was to develop and validate a non-invasive approach to estimate scapular kinematics in individual patients. We hypothesized that machine learning algorithms could be developed using motion capture data to accurately estimate dynamic scapula orientation based on measured humeral orientations and acromion process positions. The accuracy of the algorithms was evaluated against a gold standard of biplane fluoroscopy using a 2D to 3D fluoroscopy/model matching process. Individualized neural networks were developed for nine healthy adult shoulders. These models were used to predict scapulothoracic kinematics, and the predicted kinematics were compared to kinematics obtained using biplane fluoroscopy to determine the accuracy of the machine learning algorithms. Results showed correlations between predicted kinematics and validation kinematics. Estimated kinematics were within 10 of validation kinematics. We concluded that individualized machine learning algorithms show promise for providing accurate, non-invasive measurements of scapulothoracic kinematics. |
| Databáze: | OpenAIRE |
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