Prediction of Class III treatment outcomes through orthodontic data mining
Prediction of Class III treatment outcomes through orthodontic data mining
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DOI：
10.1093/ejo/cju038
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发表时间：
2015-06-01
影响因子：
2.6
通讯作者：
Franchi, Lorenzo
中科院分区：
文献类型：
--
作者：
Auconi, Pietro;Scazzocchio, Marco;Franchi, Lorenzo
目的：为了确定是否有可能预测第三类治疗结果的基础上，从一个组合的计算分析，来自复杂性科学，如模糊聚类再分配和网络分析。方法：54例III类患者的头影测量数据对32名女性和22名男性患者在早期快速上颌扩弓和面罩治疗后（T1，平均年龄8.2 ± 1.6岁）和固定矫治器治疗后（T2，平均年龄14.6 ± 1.8岁）进行了分析。患者在T1分类的基础上，高会员等级成三个主要的牙-骨模糊聚类表型：过度发散（HD），下颌（HM），和平衡（Bal）表型。结果：54例患者中有9例（16.7%）未成功，其中11例（16.7%）未成功。一旦患者被框定到其聚类成员中，则不成功病例的个体化治疗前预测在很大程度上是不同的：HD和HM患者显示出比Bal患者显著更高的不成功病例患病率（Bal聚类中为0%，HM聚类中为28.6%，HD聚类中为33.3%）。网络分析捕获了一些明显的相互依赖性的III类患者，显示了一个更连接的交互结构的头影测量数据集在HM和HD患者与Bal患者相比。最大限度地减少模型中的头影测量变量之间的几何连接后，结果得到证实。结论：模糊聚类再划分可以有效地用于估计不成功的治疗结果在III类患者的个性化风险。
Objective: To determine whether it is possible to predict Class III treatment outcomes on the basis of a model derived from a combination of computational analyses derived from complexity science, such as fuzzy clustering repartition and network analysis.Methods: Cephalometric data of 54 Class III patients (32 females, 22 males) taken before (T1, mean age 8.2 +/- 1.6 years) and after (T2, mean age 14.6 +/- 1.8 years) early rapid maxillary expansion and facemask therapy followed by fixed appliances were analysed. Patients were classified at T1 on the basis of high membership grade into three main dentoskeletal fuzzy cluster phenotypes: hyperdivergent (HD), hypermandibular (HM), and balanced (Bal) phenotypes. The prevalence rate of successful and unsuccessful cases at T2 was calculated for the three clusters and compared by means of Fisher's exact test corrected for multiple testing (Holm-Bonferroni method).Results: Unsuccessful cases were 9 out of 54 patients (16.7%). Once patients were framed into their cluster membership, the individualized pre-treatment prediction of unsuccessful cases was largely differentiated: HD and HM patients showed a significantly greater prevalence rate of unsuccessful cases than Bal patients (0% in Bal cluster, 28.6% in HM cluster, and 33.3% in HD cluster). Network analysis captured some noticeable interdependencies of Class III patients, showing a more connected interactive structure of cephalometric data sets in HM and HD patients compared with Bal patients. The results were confirmed after minimizing the geometrical connections between cephalometric variables in the model.Conclusions: Fuzzy clustering repartition can be usefully used to estimate an individualized risk of unsuccessful treatment outcome in Class III patients.