Detecting the presence of anterior cruciate ligament deficiency based on a double pendulum model, intrinsic time-scale decomposition (ITD) and neural networks

Efficient Subject-Independent Detection of Anterior Cruciate Ligament Deficiency Based on Marine Predator Algorithm and Support Vector Machine

Anterior cruciate ligament (ACL) deficiency not only reduces knee stability, but also increases the risk of more disease and impairs daily life, thus requiring efficient detection of ACL deficiency. To build an efficient subject-independent ACL deficiency detection model, this study proposes a new method called SVM-MPA that fuses marine predator algorithm (MPA) and support vector machine (SVM) for simultaneous feature selection, hyperparameter optimization and classification. 35ACL-deficient (ACLD) and 35 ACL-intact (ACLI) participants were recruited to collect 6-degree-of-freedom knee kinematic data. Then, 216-dimensional multi-domain features covering time domain, frequency domain, time-frequency domain and nonlinearity were extracted. The error rate of SVM classification based on 5-fold cross-validation was used to construct the fitness of MPA, and MPA served to select features and optimize two hyperparameters for SVM. The majority voting strategy-based post-processing was introduced to convert the gait cycle-level to knee-level ACL deficiency detection. Comparing with 7 well-known meta-heuristic algorithms and running all 20 times, the best average gait cycle-level ACL deficiency detection performance (sensitivity: 96.78±0.4.84%, specificity: 99.43±5.70%, and accuracy: 98.48±1.70%) was obtained using the proposed method. With post-processing, this study improved the best (final) detection performance (sensitivity: 97.78±4.97%, specificity: 100±0.00%, and accuracy: 99.13±1.94%). These results demonstrate the feasibility and effectiveness of the proposed method and shows that an efficient subject-independent ACL deficiency detection model can be constructed using the proposed method, which makes it possible to provide a non-invasive, objective and accurate preoperative auxiliary detection method for diagnosing ACL deficiency clinically.

Sports Injury Modeling of the Anterior Cruciate Ligament Based on the Intelligent Finite Element Algorithm

In order to solve the problem of sports injury modeling of the anterior cruciate ligament, a method based on the intelligent finite element algorithm is proposed. Considering the transverse isotropy of the ligament, this paper constructs a 3D finite element model of the knee joint based on medical image data. The same ligament constitutive equation was used to fit the parameters of stress-strain mechanical experimental curves of three different anterior cruciate ligaments, and the effects of different anterior cruciate ligament mechanical parameters on kinematics and biomechanical properties of the knee joint were compared. The experimental results show that, in models 1, 2, and 3, the maximum stress values appear in the posterolateral of the femoral attachment area of the ligament, which are 16.24 MPa, 16.36 MPa, and 22.05 MPa, respectively. However, the stress values at the tibial attachment area are 9.80, 13.8, and 13.93 MPa, respectively, and the stress values at the anterolateral part of the middle ligament are 6.36, 11.89, and 12.26 MPa, respectively, which are all smaller than those at the femoral attachment area, which also quantitatively explains the clinical phenomenon that ACL fracture often occurs in the femoral attachment area in practice. Thus, the three-dimensional finite element model of the knee joint highly simulates the structure and material properties of the knee joint. This method proves that the intelligent finite element algorithm can effectively solve the modeling problem of sports injury of the anterior cruciate ligament.