Background: Lateral elbow tendinopathy (LET) is prevalent in the upper extremity, with various therapeutic options. Understanding the types and the relations between the radiological tendon features would help to develop more specific treatments. This study reviewed ultrasound exams of LET to investigate the types of degenerative findings and the relationships between them in one of the most prominent sports medicine clinics in Latin America. Methods: A retrospective study was performed. We evaluated 4335 ultrasonographic exams with LET from 2017 and 2018. Five principal degenerative ultrasound criteria with subtypes were selected: hypoechogenicity, neovascularity, calcification, enthesopathy, and intrasubstance tear. A multiple linear regression model was conducted to explore the association between the findings, sex, and age. Results: Overall, 4324 ultrasound exams were analyzed; 2607 (60.29%) were males. Multiple degenerative tendon findings were found in adults (≥18 years) with LET. Hypoechogenicity (67.77%) and neovascularity (37.8%) were the most frequent. The mean length of a tendon tear in both sexes was 4.44 (± 2.81) millimeters. Mild hypoechogenicity (P < .001), and depth intrasubstance tear (P < .01) were statistically significant between them. Severe hypoechogenicity was associated with an increase in all tendon tear dimensions for length 1.37 ([95% Confidence interval (CI), 0.57, 2.17]; P < .001), for width 1.10 ([95% CI, 0.33, 1.87]; P < .01) and for depth 1.64 ([95% CI, 0.40, 2.88]; P < .01). Additional findings associated with an increase in the length dimension were 0.42 associated with focal neovascularity ([95% CI, 0.19, 0.65]; P < .001), and 0.71 associated with multiple neovascularity ([95% CI, 0.27, 1.15]; P < .01). Conclusions: Hypoechogenicity and neovascularity findings presented a positive association with the size of tendon tear in patients with LET. This study reaffirms the increased predominance of tendon tear during the 4th to 6th decades of life.

Background: The SARS-CoV-2 virus has demonstrated the weakness of many health systems worldwide, creating a saturation and lack of access to treatments. A bottleneck to fight this pandemic relates to the lack of diagnostic infrastructure for early detection of positive cases, particularly in rural and impoverished areas of developing countries. In this context, less costly and fast machine learning (ML) diagnosis-based systems are helpful. However, most of the research has focused on deep-learning techniques for diagnosis, which are computationally and technologically expensive. ML models have been mainly used as a benchmark and are not entirely explored in the existing literature on the topic of this paper. Objective: To analyze the capabilities of ML techniques (compared to deep learning) to diagnose COVID-19 cases based on X-ray images, assessing the performance of these techniques and using their predictive power for such a diagnosis. Methods: A factorial experiment was designed to establish this power with X-ray chest images of healthy, pneumonia, and COVID-19 infected patients. This design considers data-balancing methods, feature extraction approaches, different algorithms, and hyper-parameter optimization. The ML techniques were evaluated based on classification metrics, including accuracy, the area under the receiver operating characteristic curve (AUROC), F1-score, sensitivity, and specificity. Results: The design of experiment provided the mean and its confidence intervals for the predictive capability of different ML techniques, which reached AUROC values as high as 90% with suitable sensitivity and specificity. Among the learning algorithms, support vector machines and random forest performed best. The down-sampling method for unbalanced data improved the predictive power significantly for the images used in this study. Conclusions: Our investigation demonstrated that ML techniques are able to identify COVID-19 infected patients. The results provided suitable values of sensitivity and specificity, minimizing the false-positive or false-negative rates. The models were trained with significantly low computational resources, which helps to provide access and deployment in rural and impoverished areas.

The aim of this study was to describe a perineural ultrasound-guided infiltration technique for management of radial tunnel syndrome and to report its preliminary results in 54 patients. A mixture of a saline solution, a local anesthetic, and a corticosteroid solution was infiltrated in the perineural region at the arcade of Frohse. Pain was reported in 100% of patients before the procedure versus 1.9% after the procedure. Scratch collapse and Cozen test results were positive in 98.1% and 66.7% of patients before infiltration, respectively, versus 5.6% and 9.2% after infiltration. All variables had statistically significant differences between preprocedure and postprocedure evaluations (P < .01).