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 differentiate low versus high grade PS. Methods: Ultrasound bone tracking kinematic data were collected during SSS manoeuvres in 23 ACL injured patients using the GATOR device, and ground truth PS grades (0 to 3) were assigned under general anaesthesia by fellowship trained orthopaedic sports surgeons. From the data collected, Leave one out cross validation (LOOCV) was used to train the ML classifier. Clinical SSS data from 6 ACL deficient patients was used for independent held out validation of their low grade (Grade 1) versus high grade (Grade 2/3) PS. Multiple deep learning architectures (XceptionTime, InceptionTime, FCN, ResNet, ResCNN) and training strategies (including mixup augmentation and supervised contrastive learning) were tested. Performance was measured by one versus rest (OVR) AUC under LOOCV and by AUC (low vs high grade PS) from the held out patient sessions. Results: The ML classifier achieved a maximum OVR AUC of 0.928 under LOOCV. Classifier performance increased with pivot-shift severity: Grade 3 was identified most reliably (AUC ~0.81; sensitivity 0.70 to 0.80), whereas Grade 2 remained the most challenging boundary (sensitivity 0.20 to 0.75 across configurations). For the clinically relevant binary classification of low versus high grade pivot shift, the classifier generalised well to a completely unseen patient cohort (AUC 0.889; accuracy 0.860; sensitivity 0.850; minimum class sensitivity 0.767). Conclusion: The study demonstrates that kinematic ultrasound bone tracking during sit stand sit contains transferable information about rotational instability severity in ACL deficient patients, and represents the first reported approach to predict pivot shift grade from a patient performed functional movement. The strong cross validation performance confirms that the signals contain meaningful PS grade discriminative information, but larger datasets targeting 50 to 100 sessions per grade will be required to achieve patient level generalisation and advance this novel rotational instability assessment tool toward full clinical adoption.