Li Haoran, Huang Hongshi, Ren Shuang, Rong Qiguo
Department of Mechanics and Engineering Science, College of Engineering, Peking University, Beijing 100871, China.
Department of Sports Medicine, Peking University Third Hospital, Institute of Sports Medicine of Peking University, Beijing 100871, China.
Bioengineering (Basel). 2023 Feb 22;10(3):284. doi: 10.3390/bioengineering10030284.
Patients with anterior cruciate ligament (ACL) deficiency (ACLD) tend to have altered lower extremity kinematics and dynamics. Clinical diagnosis of ACLD requires more objective and convenient evaluation criteria. Twenty-five patients with ACLD before ACL reconstruction and nine healthy volunteers were recruited. Five experimental jogging data sets of each participant were collected and calculated using a musculoskeletal model. The resulting knee flexion and muscle force data were analyzed using a -test for characteristic points, which were the time points in the gait cycle when the most significant difference between the two groups was observed. The data of the characteristic points were processed with principal component analysis to generate a composite index for multivariable linear regression. The accuracy rate of the regression model in diagnosing patients with ACLD was 81.4%. This study demonstrates that the multivariable linear regression model and composite index can be used to diagnose patients with ACLD. The composite index and characteristic points can be clinically objective and can be used to extract effective information quickly and conveniently.
前交叉韧带（ACL）损伤（ACLD）患者的下肢运动学和动力学往往会发生改变。ACLD的临床诊断需要更客观、便捷的评估标准。招募了25例ACL重建术前的ACLD患者和9名健康志愿者。使用肌肉骨骼模型收集并计算了每位参与者的五组实验慢跑数据集。使用t检验对所得的膝关节屈曲和肌肉力量数据进行特征点分析，特征点是在步态周期中观察到两组之间最显著差异的时间点。对特征点的数据进行主成分分析，以生成用于多变量线性回归的综合指数。回归模型诊断ACLD患者的准确率为81.4%。本研究表明，多变量线性回归模型和综合指数可用于诊断ACLD患者。综合指数和特征点在临床上具有客观性，可快速、便捷地提取有效信息。
