Hwang Ui-Jae, Kim Jin-Seong, Chung Kyu Sung
Department of Physical Therapy, College of Health Science, Laboratory of KEMA AI Research (KAIR), Yonsei University, Wonju, South Korea.
Department of Orthopaedic Surgery, Hanyang University Hospital at Guri, Gyeonggi-do, Republic of Korea.
Orthop J Sports Med. 2025 Mar 3;13(3):23259671251319512. doi: 10.1177/23259671251319512. eCollection 2025 Mar.
Anterior cruciate ligament (ACL) reconstruction (ACLR) aims to restore knee stability and function; however, recovery outcomes vary widely, highlighting the need for predictive tools to guide rehabilitation and patient readiness.
To identify the most effective machine learning models for predicting the successful recovery of Patient Acceptable Symptom State (PASS) in terms of subjective function, symptoms, and psychological readiness 12 months after ACLR using physical performance measures obtained 3 months after ACLR.
Cohort study; Level of evidence, 3.
The authors retrospectively analyzed the data of 113 patients who underwent single-bundle anatomic ACLR. Physical performance measures at 3 months after ACLR included the Y-balance and isokinetic muscle strength tests. The successful recovery of PASS outcomes at 12 months were assessed using the International Knee Documentation Committee (IKDC) and the ACL-Return to Sport after Injury (ACL-RSI) scale. Five machine learning algorithms were assessed: logistic regression, decision tree, random forest, gradient boosting, and support vector machines.
The gradient boosting model demonstrated the highest area under the curve (AUC) scores for predicting SRPAS of the IKDC (AUC, 0.844; F1, 0.889), and the random forest model demonstrated the highest AUC scores for predicting the successful recovery of PASS of the ACL-RSI (AUC, 0.835; F1, 0.732) during test models. Key predictors of the successful recovery of PASS outcomes included young age and low deficits in the 60 deg/s flexor and extensor peak torque for the IKDC, low 180 deg/s extensor and flexor mean power deficit, and low 60 deg/s flexor peak torque deficits for the ACL-RSI.
Machine learning showed that younger age and greater 3-month isokinetic strength at 60 deg/s predicted attainment of the successful recovery of PASS of the IKDC at 1 year after ACL. Greater 3-month isokinetic strength at 180 deg/s was most predictive of attaining the successful recovery of PASS of the ACL-RSI at 12 months.
前交叉韧带（ACL）重建术（ACLR）旨在恢复膝关节稳定性和功能；然而，恢复结果差异很大，这凸显了需要预测工具来指导康复治疗和患者的康复准备情况。
使用ACLR术后3个月获得的身体性能指标，确定最有效的机器学习模型，以预测ACLR术后12个月患者在主观功能、症状和心理准备方面达到患者可接受症状状态（PASS）的成功恢复情况。
队列研究；证据等级，3级。
作者回顾性分析了113例行单束解剖型ACLR患者的数据。ACLR术后3个月的身体性能指标包括Y平衡测试和等速肌力测试。使用国际膝关节文献委员会（IKDC）和ACL损伤后恢复运动（ACL-RSI）量表评估12个月时PASS结果的成功恢复情况。评估了五种机器学习算法：逻辑回归、决策树、随机森林、梯度提升和支持向量机。
在测试模型中，梯度提升模型在预测IKDC的SRPAS方面表现出最高的曲线下面积（AUC）得分（AUC，0.844；F1，0.889），随机森林模型在预测ACL-RSI的PASS成功恢复方面表现出最高的AUC得分（AUC，0.835；F1，0.732）。PASS结果成功恢复的关键预测因素包括年龄较小以及IKDC中60°/s屈伸肌峰值扭矩的低差值、180°/s伸肌和屈肌平均功率差值较低以及ACL-RSI中60°/s屈肌峰值扭矩差值较低。
机器学习表明，年龄较小以及60°/s时3个月等速肌力较强可预测ACLR术后1年IKDC的PASS成功恢复。180°/s时3个月等速肌力较强最能预测12个月时ACL-RSI的PASS成功恢复。
