Prediction of Pivot Shift Grade Using In-Vivo Ultrasound Bone Tracking During Sit-Stand-Sit: A Machine Learning Feasibility Study

Background: The pivot shift (PS) test is the most specific clinical examination for anterolateral rotational instability in ACL deficient knees, yet grading remains subjective, as evidenced by poor interobserver reliability, particularly for Grade 2. Since low grade (Grade 1) versus high grade (Grades 2/3) PS is the threshold for recommending lateral extra articular augmentation, performing the test in awake clinic patients limits grading reproducibility and introduces variability in surgical decision making. Existing methods to quantify the pivot shift usually require examiner performed testing under general anaesthesia. No prior approach has ascertained PS grading from a separate patient performed functional movement. Purpose: To evaluate the feasibility of a machine learning (ML) classifier, trained on kinematic ultrasound bone tracking signals acquired during a patient sit stand sit (SSS) knee movement, to predict their PS grade, and to clinically validate its ability to differenti