Objectives To evaluate the feasibility of using wearable inertial measurement units (IMUs; small body-worn sensors that capture linear acceleration and angular velocity) combined with machine learning (ML) to identify anterior cruciate ligament (ACL) injuries during clinical knee joint laxity assessments. Design Prospective exploratory feasibility study using a case–control design. A feed-forward neural network classifier was trained on time-normalised IMU signals recorded during Lachman and Anterior Drawer tests to distinguish ACL-injured from healthy knees. Model performance was assessed using 10-fold participant-level cross-validation and reported at repetition-wise and subject-wise levels. Setting Biomechanics laboratory within a secondary care setting in London, UK. Recruitment occurred through an acute soft-tissue injury management clinic. Participants 50 participants were recruited: 26 healthy controls and 24 individuals with an MRI-confirmed ACL injury (partial or complete) sustained within the current injury episode. Healthy controls contributed 52 uninjured legs and ACL-injured participants contributed 25 injured and 23 contralateral uninjured legs (including 1 bilateral injury). Inclusion criteria for the injured group were acute knee injury, MRI-confirmed ACL tear and ability to bear weight. Controls were ≥18 years with no history of knee ligament injury. Exclusion criteria included age 45, chronic joint conditions, non-weightbearing status, pregnancy, allergy to adhesives or inability to provide informed consent. All participants completed the study. Interventions Not applicable. Participants underwent standardised clinical Lachman and Anterior Drawer tests performed by a specialist physiotherapist while IMUs were mounted on the femur and tibia using a custom three-dimensional-printed rig. Primary and secondary outcome measures The primary outcome was diagnostic accuracy of an ML model classifying ACL injury status (injured vs healthy) based on IMU-derived linear acceleration and angular velocity (three axes per sensor). Secondary outcomes included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic curve (AUROC) and F1 score. Planned model assessments (repetition-wise and subject-wise) were completed as intended. Hyperparameter adjustments were made post hoc to address overfitting when expanding to the full dataset. Results Across the full dataset (n=50), the comparative leg model (method 2) achieved a subject-wise diagnostic accuracy of 81%, sensitivity 83%, specificity 79%, PPV 80% and NPV 83% using 10-fold participant-level cross-validation. Repetition-wise accuracy was 71%. Discriminative performance was moderate (AUROC 0.773), with an F1 score of 0.889. The individual leg model (method 1) showed lower performance (subject-wise accuracy 70%; sensitivity 43%; specificity 81%). Conclusions This study supports the feasibility of wearable sensors to be used in the clinical assessment of ACL injury to assist in capturing movement features associated with joint laxity. More work should be done to increase generalisability and validate findings further.
Allott et al. (Mon,) studied this question.