Standardizing and Classifying Anterior Cruciate Ligament Injuries: An International Multicenter Study Using a Mobile Application

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.