Background/Objectives: This international multicenter study aimed to assess the effectiveness of the Pivot-Shift Meter (PSM) mobile application in diagnosing and classifying anterior cruciate ligament (ACL) injuries, emphasizing the need for standardization to improve diagnostic precision and treatment outcomes. Methods: ACL evaluations were conducted by eight experienced orthopedic surgeons across five Latin American countries (Bolivia, Chile, Colombia, Ecuador, and Mexico). The PSM app utilized smartphone gyroscopes and accelerometers to standardize the pivot-shift test. Data analysis from 224 control tests and 399 standardized tests included non-parametric statistical methods, such as the Mann–Whitney U test for group comparisons and chi-square tests for categorical associations, alongside neural network modeling for injury grade classification. Results: Statistical analysis demonstrated significant differences between standardized and control tests, confirming the effectiveness of the standardization. The neural network model achieved high classification accuracy (94.7%), with precision, recall, and F1 scores exceeding 90%. Receiver Operating Characteristic (ROC) analysis yielded an area under the curve of 0.80, indicating reliable diagnostic accuracy. Conclusions: The PSM mobile application, combined with standardized pivot-shift techniques, is a reliable tool for diagnosing and classifying ACL injuries. Its high performance in predicting injury grades makes it a valuable addition to clinical practice for enhancing diagnostic precision and informing treatment planning.

This international multicenter study is aimed to assess the effectiveness of the Pivot-Shift Meter (PSM) mobile application in diagnosing and classifying anterior cruciate ligament (ACL) injuries, with emphasis in the need for standardization in ACL injury assessment to improve diagnostic precision and enhance treatment outcomes. Methods: The study included over 600 ACL evaluations from five Latin American countries (Bolivia, Chile, Colombia, Ecuador, and Mexico) performed by eight experienced orthopedic surgeons. The PSM app, using smartphone gyroscopes and accelerometers, was employed to standardize the pivot-shift test, and the data was analyzed using MATLAB software. Tests were classified into eight classes, and a neural network model was developed to categorize the injury grades. Results: Statistical analysis demonstrated significant differences between standardized and control tests, supporting the standardization's effectiveness. The neural network model exhibited high classification accuracy (94.7%), with precision, recall, and F1 scores exceeding 90%. Additionally, the model demonstrated an ability to reliably grade ACL injuries with an area under the curve (AUC) of 0.80, indicating high diagnostic accuracy. Conclusions: The PSM mobile application, combined with standardized pivot-shift techniques, proved to be a reliable tool for diagnosing and classifying ACL injuries. The neural network model showed high performance in predicting injury grades, making this method a valuable tool in clinical practice for improving diagnostic precision and treatment planning.

Anterior cruciate ligament (ACL) instability poses a considerable challenge in traumatology and orthopedic medicine, demanding precise diagnostics for optimal treatment. The pivot-shift test, a pivotal assessment tool, relies on subjective interpretation, emphasizing the need for supplementary imaging. This study addresses this limitation by introducing a machine learning classification algorithm integrated into a mobile application, leveraging smartphones’ built-in inertial sensors for dynamic rotational stability assessment during knee examinations. Orthopedic specialists conducted knee evaluations on a cohort of 52 subjects, yielding valuable insights. Quantitative analyses, employing the Intraclass Correlation Coefficient (ICC), demonstrated robust agreement in both intraobserver and interobserver assessments. Specifically, ICC values of 0.94 reflected strong concordance in the timing between maneuvers, while signal amplitude exhibited consistency, with the ICC ranging from 0.71 to 0.66. The introduced machine learning algorithms proved effective, accurately classifying 90% of cases exhibiting joint hypermobility. These quantifiable results underscore the algorithm’s reliability in assessing knee stability. This study emphasizes the practicality and effectiveness of implementing machine learning algorithms within a mobile application, showcasing its potential as a valuable tool for categorizing signals captured by smartphone inertial sensors during the pivot-shift test.

The Double Bundle U-DOS Reverse Technique for PCL tears is a novel arthroscopic technique that combines two well-known procedures: the mono-tunnel (a surgical approach that involves a single tunnel for PCL reconstruction) and medial-portal approaches, providing better posterior stability, improved functional outcomes, and reduced risk of posterior tibial subluxation. The technique allows the simultaneous treatment of anterolateral and posteromedial bundles and has been successfully performed on 16 patients, with follow-up showing sufficient support and resistance to posterior tibial subluxation or excessive displacement, resulting in a stable knee joint.

Aortic dissection is a life-threatening acute condition characterized by the separation of the aortic walls layers. It is caused by a tear in the internal vascular wall (intimal layer and middle layer), which results in bleeding between the layers and causes abrupt and excruciating pain. The appropriate consideration must be given to the conditions dynamic nature, and variations in clinical presentation, without neglecting the urgency for intervention. In this case study, a 65-year-old male engaged in a car accident is admitted to urgent care with a traumatic aortic dissection diagnosis that included the aortic arch, a segmental exposed fracture of 1/3 distal of the right femur AO 32C3k, and an intertrochanteric fracture AO 31A1.3. The patient developed transient paraplegia as the initial manifestation of acute aortic dissection, which represents a high mortality and morbidity entity without adequate and prompt treatment, and prompt diagnosis and management were critical. A patient with severe thoracic and abdominal trauma caused by high-energy injury should be properly evaluated for the possibility of traumatic aortic dissection. The endovascular aortic repair was performed, resulting in a positive clinical evolution due to the important participation of the multidisciplinary trauma team involved in patient management and prompted decision-making.