Ciliberti Federica Kiyomi, Cesarelli Giuseppe, Guerrini Lorena, Gunnarsson Arnar Evgeni, Forni Riccardo, Aubonnet Romain, Recenti Marco, Jacob Deborah, Jónsson Halldór, Cangiano Vincenzo, Islind Anna Sigríður, Gambacorta Monica, Gargiulo Paolo
Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik.
Department of Chemical, Materials and Production Engineering (DICMaPI), University of Naples Federico II, Naples.
Eur J Transl Myol. 2022 Jun 28;32(2):10678. doi: 10.4081/ejtm.2022.10678.
Knee Osteoarthritis (OA) is a highly prevalent condition affecting knee joint that causes loss of physical function and pain. Clinical treatments are mainly focused on pain relief and limitation of disabilities; therefore, it is crucial to find new paradigms assessing cartilage conditions for detecting and monitoring the progression of OA. The goal of this paper is to highlight the predictive power of several features, such as cartilage density, volume and surface. These features were extracted from the 3D reconstruction of knee joint of forty-seven different patients, subdivided into two categories: degenerative and non-degenerative. The most influent parameters for the degeneration of the knee cartilage were determined using two machine learning classification algorithms (logistic regression and support vector machine); later, box plots, which depicted differences between the classes by gender, were presented to analyze several of the key features' trend. This work is part of a strategy that aims to find a new solution to assess cartilage condition based on new-investigated features.
膝关节骨关节炎（OA）是一种影响膝关节的高度普遍的病症，会导致身体功能丧失和疼痛。临床治疗主要集中在缓解疼痛和限制残疾；因此，找到评估软骨状况以检测和监测OA进展的新范式至关重要。本文的目的是突出软骨密度、体积和表面等几个特征的预测能力。这些特征是从47名不同患者膝关节的三维重建中提取的，分为两类：退行性和非退行性。使用两种机器学习分类算法（逻辑回归和支持向量机）确定了膝关节软骨退变的最具影响力参数；随后，展示了按性别描绘类别差异的箱线图，以分析几个关键特征的趋势。这项工作是旨在基于新研究的特征找到评估软骨状况新解决方案的策略的一部分。
