Diagnostic accuracy of machine-learning-assisted detection for anterior cruciate ligament injury based on magnetic resonance imaging: Protocol for a systematic review and meta-analysis

Focal Versus Combined Focal Plus Radial Extracorporeal Shockwave Therapy in Lateral Elbow Tendinopathy: A Retrospective Study

Background: Lateral epicondylitis of the elbow, commonly known as tennis elbow, is a musculoskeletal disorder characterized by pain and degeneration of the common extensor tendon. Despite various treatments, optimal management remains debated. Objective: This study aimed to compare the effectiveness of focal extracorporeal shockwave therapy (F-ESWT) alone versus a combination of focal and radial pressure waves (F-ESWT+R-PW) in treating chronic lateral epicondylitis. Methods: This retrospective observational study included 45 patients diagnosed with chronic lateral epicondylitis divided into two groups based on the treatment received: group A (F-ESWT, n = 23) and group B (F-ESWT+R-PW, n = 22). Both groups underwent three weekly sessions of their respective treatments. Patients were also given a home exercise protocol. Primary outcomes were assessed using the Visual Analog Scale (VAS) for pain and the Patient-Rated Tennis Elbow Evaluation (PRTEE) for pain and functional impairment at baseline (T0), 4 weeks (T1), 12 weeks (T2), and 24 weeks (T3) post-treatment. Secondary outcomes included grip strength and ultrasonographic measurements of common extensor tendon (CET) thickness and vascularization. Results: Significant improvements in VAS and PRTEE scores were observed in both groups at all follow-up points. Group B showed greater pain reduction at T1 (VAS: 3.0 ± 1.6 vs. 4.43 ± 1.47; p < 0.005) and T2 (VAS: p < 0.030) compared to group A. Functional outcomes (PRTEE) also favored group B at T1 (p < 0.030) and in the pain section at T2 (p < 0.020). Grip strength improved similarly in both groups. CET thickness showed no significant differences at T3. Vascularization decreased significantly in both groups, with a non-significant trend favoring group B. Conclusions: The combined F-ESWT+R-PW therapy proved more effective than F-ESWT alone in the short- to mid-term management of chronic lateral epicondylitis, significantly enhancing pain reduction and functional outcomes. The combination of focal and radial pressure waves offers a superior therapeutic approach, leveraging the distinct mechanisms of each modality for better clinical results. Further research is needed to confirm these findings and establish long-term efficacy.

Machine Learning in Healthcare

Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, amongst a few. Although, skepticism remains regarding the practical application and interpretation of results from ML-based approaches in healthcare settings, the inclusion of these approaches is increasing at a rapid pace. Here we provide a brief overview of machine learning-based approaches and learning algorithms including supervised, unsupervised, and reinforcement learning along with examples. Second, we discuss the application of ML in several healthcare fields, including radiology, genetics, electronic health records, and neuroimaging. We also briefly discuss the risks and challenges of ML application to healthcare such as system privacy and ethical concerns and provide suggestions for future applications.

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.

A Machine-Learning Approach to Measure the Anterior Cruciate Ligament Injury Risk in Female Basketball Players

Anterior cruciate ligament (ACL) injury represents one of the main disorders affecting players, especially in contact sports. Even though several approaches based on artificial intelligence have been developed to allow the quantification of ACL injury risk, their applicability in training sessions compared with the clinical scale is still an open question. We proposed a machine-learning approach to accomplish this purpose. Thirty-nine female basketball players were enrolled in the study. Leg stability, leg mobility and capability to absorb the load after jump were evaluated through inertial sensors and optoelectronic bars. The risk level of athletes was computed by the Landing Error Score System (LESS). A comparative analysis among nine classifiers was performed by assessing the accuracy, F1-score and goodness. Five out nine examined classifiers reached optimum performance, with the linear support vector machine achieving an accuracy and F1-score of 96 and 95%, respectively. The feature importance was computed, allowing us to promote the ellipse area, parameters related to the load absorption and the leg mobility as the most useful features for the prediction of anterior cruciate ligament injury risk. In addition, the ellipse area showed a strong correlation with the LESS score. The results open the possibility to use such a methodology for predicting ACL injury.

Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach

The most commonly injured ligament in the human body is an anterior cruciate ligament (ACL). ACL injury is standard among the football, basketball and soccer players. The study aims to detect anterior cruciate ligament injury in an early stage via efficient and thorough automatic magnetic resonance imaging without involving radiologists, through a deep learning method. The proposed approach in this paper used a customized 14 layers ResNet-14 architecture of convolutional neural network (CNN) with six different directions by using class balancing and data augmentation. The performance was evaluated using accuracy, sensitivity, specificity, precision and F1 score of our customized ResNet-14 deep learning architecture with hybrid class balancing and real-time data augmentation after 5-fold cross-validation, with results of 0.920%, 0.916%, 0.946%, 0.916% and 0.923%, respectively. For our proposed ResNet-14 CNN the average area under curves (AUCs) for healthy tear, partial tear and fully ruptured tear had results of 0.980%, 0.970%, and 0.999%, respectively. The proposing diagnostic results indicated that our model could be used to detect automatically and evaluate ACL injuries in athletes using the proposed deep-learning approach.