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<p>Anterior cruciate ligament (ACL) injury is a prevalent sports-related trauma with significant impact on both professional athletes and young individuals. While often viewed as accidental events, high recurrence rates suggest the presence of underlying structural risk factors. This study investigated the association between 3D bone morphology and ACL injury susceptibility. Leveraging a multicenter dataset of 5000 MRIs, we developed ACL-injury Patterning (ACL-P), a deep learning model that utilizes 3D bone morphology point clouds for injury classification. Given the short-term stability of bone geometry following trauma, this classification serves as a proxy task to quantify the correlation between pre-existing morphological traits and ACL injury through discriminative performance. The model’s discriminative performance was validated across an external civilian test set (<i>n</i> = 7797) and a professional athlete test set (<i>n</i> = 269), yielding an Area Under the Curve (AUC) of 0.887 (95% CI: 0.879–0.895) and 0.907 (95% CI: 0.872–0.941), respectively. Robustness checks confirmed that the model’s performance remained consistent across both acute and chronic injury phases (0–365 days) and was independent of secondary joint dislocation. These findings establish 3D bone morphology as a significant anatomical factor linked to injury susceptibility, enhancing our understanding of the structural correlates of knee trauma.</p>

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Bone morphology as a significant risk factor for anterior cruciate ligament injury

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月15日

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  • Dingyu Wang,
  • Chao Liu,
  • Xinwei Du,
  • Xueqing Yu,
  • Jiaxin Liu,
  • Shanggui Liu,
  • Songlin Zhang,
  • Tingting Jiang,
  • Dong Jiang

摘要

Anterior cruciate ligament (ACL) injury is a prevalent sports-related trauma with significant impact on both professional athletes and young individuals. While often viewed as accidental events, high recurrence rates suggest the presence of underlying structural risk factors. This study investigated the association between 3D bone morphology and ACL injury susceptibility. Leveraging a multicenter dataset of 5000 MRIs, we developed ACL-injury Patterning (ACL-P), a deep learning model that utilizes 3D bone morphology point clouds for injury classification. Given the short-term stability of bone geometry following trauma, this classification serves as a proxy task to quantify the correlation between pre-existing morphological traits and ACL injury through discriminative performance. The model’s discriminative performance was validated across an external civilian test set (n = 7797) and a professional athlete test set (n = 269), yielding an Area Under the Curve (AUC) of 0.887 (95% CI: 0.879–0.895) and 0.907 (95% CI: 0.872–0.941), respectively. Robustness checks confirmed that the model’s performance remained consistent across both acute and chronic injury phases (0–365 days) and was independent of secondary joint dislocation. These findings establish 3D bone morphology as a significant anatomical factor linked to injury susceptibility, enhancing our understanding of the structural correlates of knee trauma.