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

Area of Science:

• Orthopedic Surgery
• Medical Diagnostics
• Mobile Health Technology

Background:

• Anterior cruciate ligament (ACL) injuries require precise diagnosis for effective treatment.
• Standardization of clinical tests like the pivot-shift is crucial for improving diagnostic accuracy.
• Existing methods for pivot-shift testing lack standardization, potentially leading to variable diagnostic outcomes.

Purpose of the Study:

• To evaluate the effectiveness of the Pivot-Shift Meter (PSM) mobile application in diagnosing and classifying ACL injuries.
• To assess the impact of a standardized pivot-shift test using mobile technology on diagnostic precision.
• To determine the reliability of the PSM app in grading ACL injury severity.

Main Methods:

• An international multicenter study involving eight orthopedic surgeons across five Latin American countries.
• Utilized the PSM app with smartphone sensors (gyroscopes, accelerometers) to standardize the pivot-shift test.
• Employed non-parametric statistics (Mann-Whitney U, chi-square) and neural network modeling for data analysis and classification.

Main Results:

• The standardized pivot-shift test using the PSM app showed significant improvements over control tests.
• The neural network model achieved 94.7% classification accuracy for ACL injury grades, with precision, recall, and F1 scores over 90%.
• Receiver Operating Characteristic (ROC) analysis indicated reliable diagnostic accuracy with an area under the curve of 0.80.

Conclusions:

• The PSM mobile application is a reliable tool for diagnosing and classifying ACL injuries when used with standardized pivot-shift techniques.
• The app demonstrates high performance in predicting injury grades, aiding clinical decision-making.
• Integration of the PSM app can enhance diagnostic precision and inform treatment planning for ACL injuries.