Background: Patients who have undergone Anterior Cruciate Ligament Reconstruction (ACLR) have a 6-24% chance of either re-tearing or having subsequent knee surgery. To date there have been no practical validated risk prediction models that can be easily implemented into clinical workflow for re-injury risk. Micro-Doppler radar (MDR) provides a promising solution. Objective: The purpose of this study was to investigate the predictive ability of MDR to identify persons with a previous ACLR relative to an age and sex matched healthy control. Methods: ACLR patients (n=81) and controls (n=100) performed drop box jump, sit to stand (STS), and walking trials as MDR signatures were collected. A 1D Convolutional Neural Network was developed to evaluate each activity individually followed by the development of a fusion model validation using all three activities. Results: The STS model individually achieved the highest overall accuracy of 82.3%, with a sensitivity of 71.6% and specificity of 91.0%. The fusion model using all activities achieved a peak overall accuracy to detect ACLR of 86.2%, 80.3% sensitivity, and 91% specificity. Conclusions: Currently, there is no clinically validated, efficient approach to objectively evaluate human motion at the point of care. When MDR is coupled with machine learning, we have shown that it is possible to evaluate complex biomechanical asymmetries by identifying an ACLR versus control groups comparable or superior to motion capture analysis. Future research is needed to determine if MDR can be used in conjunction with risk prediction modeling.

The authors have declared no competing interest.

The U.S. Army Medical Research Acquisition Activity, 820 Chandler Street, Fort Detrick MD 21702-5014 is the awarding and administering acquisition office. This work was supported by the Department of Defense in the amount of 1,449,996.000, through the FY21, Peer Reviewed Orthopaedic Research Program under Award No. W81XWH2210684. Opinions, interpretations, conclusions and recommendations are those of the authors and are not necessarily endorsed by the Department of Defense. The project described collected and managed data with REDCap, which was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1 TR002014 and Grant UL1 TR00045. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

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Pennsylvania State University Institutional Review Board gave ethical approval for this work under STUDY00020118.

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