Risk factors of young males with physically demanding occupations having accumulated damage of anterior cruciate ligament

Wang, Yu; Liu, Xianmin; Liangbi, Xiang; Li, Chunbao

doi:10.1111/os.13276

Cited by 2 publications

(1 citation statement)

References 39 publications

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“… 1 Failure to identify ACL tears may lead to severe consequences in terms of further injuries, delayed treatment, poor recovery, and slow return to sports. 3 , 12 , 30 , 32 , 35 Because performing a clinical examination in the acute stage is frequently hampered by the substantial knee pain and swelling that follows the injury, a magnetic resonance imaging (MRI) evaluation may bring valuable information about the status of the ACL. 7 , 38 However, MRI entails access to heavily specialized and expensive equipment at medical facilities that may not be available in remote settings or on field during a game situation.…”

Section: Discussionmentioning

confidence: 99%

Identifying Anterior Cruciate Ligament Injuries Through Automated Video Analysis of In-Game Motion Patterns

Schulc,

Leite,

Csákvári

et al. 2024

Orthopaedic Journal of Sports Medicine

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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.

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Section: Discussionmentioning

confidence: 99%

Identifying Anterior Cruciate Ligament Injuries Through Automated Video Analysis of In-Game Motion Patterns

Schulc,

Leite,

Csákvári

et al. 2024

Orthopaedic Journal of Sports Medicine

View full text Add to dashboard Cite

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Demographic and Injury Characteristics as Potential Risk Factors for Anterior Cruciate Ligament Injuries: A Multicentric Cross-Sectional Study

Motififard,

Akbari Aghdam,

Ravanbod

et al. 2024

JCM

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Background: Anterior cruciate ligament (ACL) injuries are prevalent and can have debilitating consequences, with various factors potentially influencing their occurrence. This multicentric study aimed to comprehensively analyze the epidemiological characteristics of ACL injuries. We hypothesized that specific patient characteristics, such as age, sex, body mass index (BMI), and sports involvement, would be associated with distinct injury patterns and risk profiles. Methods: This cross-sectional study analyzed the medical records of 712 patients aged 15–60 diagnosed with ACL rupture. Data on demographics, injury mechanisms, associated injuries, graft type, and sports involvement were collected. Results: The majority of patients were male (93.1%), aged 15–30 years (80.2%), and overweight (66.7%). Autografts were the predominant graft choice (96.07%). Associated injuries were present in 79.5% of cases, with medial meniscus ruptures being the most common (37.36%). Sports-related (49.3%) and non-sports-related (50.7%) injuries were nearly equal, with non-contact injuries more prevalent (71.1%). In the sports-related subgroup, associated injuries emerged as a significant risk factor for ACL rupture (p = 0.014, OR = 1.596, 95% CI: 1.101–2.314), whereas non-contact mechanisms showed borderline significance (OR = 0.75, p = 0.09). Moreover, younger athletes were more susceptible to sports-related injuries (p = 0.024), with football being the primary sport involved. Conclusions: This study identified a high prevalence of concomitant injuries with ACL injury, which increased the risk of ACL injury, particularly in sports-related cases. Age-related differences in injury patterns highlight the need for age-appropriate preventive measures, especially for younger athletes participating in high-risk sports. This underscores the need for comprehensive injury assessment, targeted prevention strategies, and optimized clinical management approaches tailored to different populations’ specific characteristics and risks.

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Risk factors of young males with physically demanding occupations having accumulated damage of anterior cruciate ligament

Cited by 2 publications

References 39 publications

Identifying Anterior Cruciate Ligament Injuries Through Automated Video Analysis of In-Game Motion Patterns

Identifying Anterior Cruciate Ligament Injuries Through Automated Video Analysis of In-Game Motion Patterns

Demographic and Injury Characteristics as Potential Risk Factors for Anterior Cruciate Ligament Injuries: A Multicentric Cross-Sectional Study

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