PDF(Pubmed)
Abstract：
Anterior cruciate ligament (ACL) instability poses a considerable challenge in traumatology and orthopedic medicine, demanding precise diagnostics for optimal treatment. The pivot-shift test, a pivotal assessment tool, relies on subjective interpretation, emphasizing the need for supplementary imaging. This study addresses this limitation by introducing a machine learning classification algorithm integrated into a mobile application, leveraging smartphones\' built-in inertial sensors for dynamic rotational stability assessment during knee examinations. Orthopedic specialists conducted knee evaluations on a cohort of 52 subjects, yielding valuable insights. Quantitative analyses, employing the Intraclass Correlation Coefficient (ICC), demonstrated robust agreement in both intraobserver and interobserver assessments. Specifically, ICC values of 0.94 reflected strong concordance in the timing between maneuvers, while signal amplitude exhibited consistency, with the ICC ranging from 0.71 to 0.66. The introduced machine learning algorithms proved effective, accurately classifying 90% of cases exhibiting joint hypermobility. These quantifiable results underscore the algorithm\'s reliability in assessing knee stability. This study emphasizes the practicality and effectiveness of implementing machine learning algorithms within a mobile application, showcasing its potential as a valuable tool for categorizing signals captured by smartphone inertial sensors during the pivot-shift test.
摘要：
前交叉韧带（ACL）不稳定性在创伤学和骨科医学中提出了相当大的挑战，要求精确的诊断，以实现最佳治疗。枢轴移位测试，一个关键的评估工具，依靠主观解释，强调补充成像的必要性。本研究通过引入集成到移动应用程序中的机器学习分类算法来解决这一限制，利用智能手机内置惯性传感器在膝关节检查期间进行动态旋转稳定性评估。骨科专家对52名受试者进行了膝关节评估，产生有价值的见解。定量分析,采用类内相关系数(ICC)，在观察员内部和观察员之间的评估中都表现出了强有力的一致性。具体来说,ICC值为0.94反映了演习之间时间的强烈一致性，而信号幅度表现出一致性，ICC的范围从0.71到0.66。引入的机器学习算法被证明是有效的，对90%表现出关节过度活动的病例进行准确分类。这些可量化的结果强调了该算法在评估膝关节稳定性方面的可靠性。这项研究强调了在移动应用程序中实施机器学习算法的实用性和有效性，展示了其作为对智能手机惯性传感器在枢轴移位测试期间捕获的信号进行分类的有价值工具的潜力。
