Synthesizing Foot and Ankle Kinematic Characteristics for Lateral Collateral Ligament Injuries Detection

• Published in: IEEE access 2020, Vol.8, p.188429-188440
• Main Authors: Liu, Xin, Zhao, Chen, Zheng, Bin, Guo, Qinwei, Zhang, Zhongshi, Wulamu, Aziguli, Zhang, Dezheng
• Format: Article
• Language: English
• Subjects: Biological system modeling; Data acquisition; deep convolutional generative adversarial networks (DCGANs); Deep learning; Foot; Gallium nitride; Injuries; injuries detection; Joint and ligament injuries; Kinematic characteristics; Kinematics; Knee; lateral collateral ligament injuries (LCL injuries) of the ankle; Ligaments; long short-term memory (LSTM) networks; Machine learning; Sports medicine; Synthesis; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3029616

Deep learning has been applied in healthcare, where features of patients are read by the computer to assist with diagnosis and treatment. In sports medicine, kinematic characteristics of injuries need to be defined. Patients' data are then acquired amplified to training deep learning models. In this study, we tracked motions of lower extremities in patients with lateral collateral ligament injuries of the ankle. Key kinematic characteristics of injuries were identified by comparing patients to normal individuals. The deep convolutional generative adversarial networks (DCGANs) was employed to synthesize a modest-sized labeled dataset to avoid the problems raised from using large-scale manual labeling data. We then fed a combination of real and synthesized data to train long short-term memory (LSTM) networks to detect patients with ligament injuries. The results showed that combined data yielded a better outcome, measured by classification accuracy and f1-score, than solely using the patient data or with a large quantity of synthesized single range of motion feature.