Center for Research and Applications of Nonlinear Systems, Department of Mathematics, University of Patras, Patras, Greece.
Department of Computer Engineering, Technological Educational Institute of Epirus, Arta, Greece.
Clin Oral Implants Res. 2017 Jul;28(7):823-832. doi: 10.1111/clr.12887. Epub 2016 Jun 1.
To cluster implants placed in patients of a private practice and identify possible implant "phenotypes" and predictors of individual implant mean bone levels (IIMBL).
Clinical and radiographical variables were collected from 72 implant-treated patients with 237 implants and a mean 7.4 ± 3.5 years of function. We clustered implants using the k-means method guided by multidimensional unfolding. For predicting IIMBL, we used principal component analysis (PCA) as a variable reduction method for an ensemble selection (ES) and a support vector machines models (SVMs). Network analysis investigated variable interactions.
We identified a cluster of implants susceptible to peri-implantitis (96% of the implants in the cluster were affected by peri-implantitis) and two overlapping clusters of implants resistant to peri-implantitis. The cluster susceptible to peri-implantitis showed a mean IIMBL of 5.2 mm and included implants placed mainly in the lower front jaw and in mouths having a mean of eight teeth. PCA extracted the parameters such as number of teeth, full-mouth plaque scores, implant surface, periodontitis severity, age and diabetes as significant in explaining the data variability. ES and SVMs showed good results in predicting IIMBL (root-mean-squared error of 0.133 and 0.149, 10-fold cross-validation error of 0.147 and 0.150, respectively). Network analysis revealed limited interdependencies of variables among peri-implantitis-affected and non-affected implants and supported the hypothesis of the existence of distinct implant "phenotypes."
Two implant "phenotypes" were identified, one with susceptibility and another with resistance to peri-implantitis. Prediction of IIMBL could be achieved by using six variables.
对一家私人诊所中植入物的位置进行聚类，并确定可能的植入物“表型”和个体植入物平均骨水平（IIMBL）的预测因子。
从 72 名接受 237 个种植体治疗且功能平均 7.4±3.5 年的患者中收集了临床和影像学变量。我们使用多维展开引导的 k-均值方法对植入物进行聚类。为了预测 IIMBL，我们使用主成分分析（PCA）作为集合选择（ES）和支持向量机模型（SVMs）的变量降维方法。网络分析研究了变量间的相互作用。
我们发现了一个易发生种植体周围炎的植入物群（该群中 96%的植入物受到种植体周围炎的影响）和两个重叠的种植体周围炎抵抗群。易发生种植体周围炎的植入物群的平均 IIMBL 为 5.2mm，植入物主要放置在下颌前牙和有 8 颗牙的口腔中。PCA 提取了牙齿数量、全口菌斑评分、种植体表面、牙周炎严重程度、年龄和糖尿病等参数，这些参数在解释数据变异性方面具有重要意义。ES 和 SVMs 在预测 IIMBL 方面表现良好（均方根误差分别为 0.133 和 0.149，10 倍交叉验证误差分别为 0.147 和 0.150）。网络分析揭示了受种植体周围炎影响和不受影响的植入物之间变量的相互依存关系有限，并支持了存在不同植入物“表型”的假设。
确定了两种植入物“表型”，一种具有易感性，另一种具有抗种植体周围炎性。可以使用六个变量来预测 IIMBL。
