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Background: Failure to diagnose anterior cruciate ligament (ACL) injury during a game can delay adequate treatment and increase the risk of further injuries. Artificial intelligence (AI) has the potential to be an accurate, cost-efficient, and readily available diagnostic tool for ACL injury in in-game situations. Purpose: To develop an automated video analysis system that uses AI to identify biomechanical patterns associated with ACL injury and to evaluate whether the system can enhance the ability of orthopaedic and sports medicine specialists to identify ACL injuries on video. Study Design: Descriptive laboratory study. Methods: A total of 91 ACL injury and 38 control movement scenes from online available match recordings were analyzed. The videos were processed to identify and track athletes and to estimate their 3-dimensional (3D) poses. Geometric features, including knee flexion, knee and hip abduction, and foot and hip rotation, were extracted from the athletes’ 3D poses. A recurrent neural network algorithm was trained to classify ACL injury, using these engineered features as its input. Analysis by 2 orthopaedic surgeons examined whether providing clinical experts with the reconstructed 3D poses and their derived signals could increase their diagnostic accuracy. Results: All AI models performed significantly better than chance. The best model, which used the long short-term memory network with engineered features, demonstrated decision interpretability and good performance (F1 score = 0.63 ± 0.01, area under the receiver operating characteristic curve = 0.88 ± 0.01). The analysis by the 2 orthopaedic surgeons demonstrated improved diagnostic accuracy for ACL injury recognition when provided with system data, resulting in a 0.08 increase in combined F1 scores. Conclusion: Our approach successfully reconstructed the 3D motion of athletes from a single-camera view and derived geometry-based biomechanical features from pose sequences. Our trained AI model was able to automatically detect ACL injuries with relatively good performance and prelabel and highlight regions of interest in video footage. Clinical Relevance: This study demonstrated the feasibility of using AI to automatically evaluate in-game video footage and identify dangerous motion patterns. Further research can explore the full potential of the biomechanical markers and use of the system by nonspecialists, potentially diminishing the rate of missed diagnosis and the detrimental outcomes that follow.
Schulc et al. (Fri,) studied this question.