Ultrasonographic Technique, Appearance, and Diagnostic Accuracy for Common Elbow Sports Injuries

The Role of Ultrasound in the Evaluation of Elbow Medial Ulnar Collateral Ligament Injuries in Throwing Athletes

PURPOSE OF REVIEW

Although ultrasound (US) imaging is commonly used to evaluate the elbow medial ulnar collateral ligament (mUCL) in throwing athletes, significant technical heterogeneity exists in the published literature and in practice. This has resulted in variable and often ambiguous US diagnostic criteria for mUCL injury. This review summarizes the literature on sonographic evaluation of the mUCL and outlines recommendations for consistent descriptive terminology, as well as future clinical and research applications.

RECENT FINDINGS

Both acute and chronic throwing loads in overhead athletes cause the mUCL to become thicker and more lax on stress testing, and these changes tend to revert after a period of prolonged rest. Stress US (SUS) can aid in the diagnosis of mUCL tears and may help identify athletes at risk of mUCL injury. Variability exists in terminology, elbow flexion angle, amount of stress applied, and technique of stress testing. Recent studies have suggested an injured elbow stress delta (SD-change in ulnohumeral joint (UHJ) space with valgus stress) of 2.4 mm and a stress delta difference (SDD-side-side difference in SD) of 1 mm each denote abnormal UHJ laxity due to mUCL injury. US imaging is a powerful and widely accessible tool in the evaluation elbow mUCL injuries. Sonologists should consider how their US techniques compare with published methods and use caution when applying diagnostic criteria outside of those circumstances. Currently, an SD of 2.4 mm and an SDD of 1 mm provide the best diagnostic accuracy for mUCL tears requiring surgery. Finally, preliminary work suggests that shear wave elastography may be helpful in evaluating the biomechanical properties of the mUCL, but additional research is needed.

Medial Ulnar Collateral Ligament Injuries in Contact Athletes

PURPOSE OF REVIEW

The purpose of this article is to review medial ulnar collateral ligament (UCL) injuries in contact athletes. UCL injuries in overhead throwing athletes are typically chronic attenuation due to repetitive valgus stress on the elbow during the throwing motion. As such, UCL reconstruction is commonly performed for these athletes. In contrast, UCL injuries in contact athletes are usually acute ligament tears or avulsions of a ligament with otherwise normal tissue. Nonoperative treatment is typically the first-line treatment for partial injuries. UCL repair may work well for acute complete injuries and may avoid the donor site morbidity of UCL reconstruction.

RECENT FINDINGS

Most of the literature regarding UCL injuries have been performed in baseball players. Historically, UCL repair has had poor outcomes in baseball players due to the chronic ligament attenuation. Therefore, much of the recent literature has focused on outcomes of UCL reconstruction, which are generally excellent. However, there is a paucity of literature studying outcomes of UCL injuries in contact athletes and those studying UCL repair. One recent study looked at a new technique for UCL repair with collagen-coated fiber tape augmentation in baseball players and found good short-term outcomes. UCL injuries in contact athletes occur typically as acute tears or avulsions. While UCL reconstruction has typically been recommended as the accepted treatment for UCL tears that require operative treatment, UCL repair may be a good alternative in contact athletes.

Machine Leaning-Based Optimization Algorithm for Myocardial Injury under High-Intensity Exercise in Track and Field Athletes

In order to train at high-intensity, athletics can again cause varying degrees of myocardial damage. Evaluating the balance between exercise myocardial injury and exercise intensity should actively prevent myocardial injury caused by high-intensity athletic training. In this paper, an intelligent optimization algorithm is used to investigate the degree of myocardial injury. The basic idea is to define the measured data and the output of the numerical model as an objective function of the structural parameters, to obtain the structural parameters by finding ways to continuously optimize the objective function to be close to the observed values, and to identify the injury based on the changes in these parameters before and after myocardial injury. The objective function can be defined in various ways, and the myocardial injury optimization algorithm can be chosen. In order to obtain the best computational results, numerical simulations of damage identification are performed using the objective function and three machine learning-based optimization algorithms. The computational results show that the combination of the objective function and the machine learning algorithms provides good accuracy and computational speed in identifying myocardial injury.