Artificial Neural Networks for the Diagnosis of Aggressive Periodontitis Trained by Immunologic Parameters
Artificial Neural Networks for the Diagnosis of Aggressive Periodontitis Trained by Immunologic Parameters
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DOI：
10.1371/journal.pone.0089757
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发表时间：
2014-03-06
期刊：
影响因子：
3.7
通讯作者：
Loos, Bruno G.
中科院分区：
文献类型：
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作者：
Papantonopoulos, Georgios;Takahashi, Keiso;Loos, Bruno G.
侵袭性牙周炎(AGP)和慢性牙周炎(CP)患者既没有单一的临床、微生物学、组织病理学或遗传学测试，也没有它们的组合。我们的目标是估计牙周炎患者临床和免疫学数据集的概率密度函数，并构建人工神经网络(ANN)以正确地将患者分为AGP或CP类。用Akaike信息准则(AIC)检验数据集上概率分布的适合性。神经网络的训练是通过交叉熵(CE)值来估计的，该值介于显示特定水平的免疫学参数的概率和核密度估计(KDE)提出的参考模式概率之间。通过10次交叉验证确定了神经网络的权值衰减正则化参数。KDE揭示了两组患者横断面和纵向骨丢失测量的可能证据。在CD_4/CD_8比值、CD_3、单核细胞、嗜酸性粒细胞、中性粒细胞和淋巴细胞计数、单核细胞IL-1、IL-2、IL-4、干扰素-γ和肿瘤坏死因子-α水平，以及抗放线菌伴生菌抗体水平上显示2~7个聚集性。和牙龈假单胞菌(P.G.)神经网络在将患者分为AGP或CP时，准确率为90%-98%。以单核细胞数、嗜酸性粒细胞数、中性粒细胞计数和CD_4/CD_8比值为输入的人工神经网络预测效果最好。当以基于KDE的CE值喂养时，神经网络可以有效地将牙周炎患者分类为AGP或CP。因此，神经网络可以通过相对简单和方便地获得的参数，如外周血中的白细胞计数，来准确地诊断AGP或CP。这将使临床医生能够更好地为他们的AGP和CP患者调整特定的治疗方案。
There is neither a single clinical, microbiological, histopathological or genetic test, nor combinations of them, to discriminate aggressive periodontitis (AgP) from chronic periodontitis (CP) patients. We aimed to estimate probability density functions of clinical and immunologic datasets derived from periodontitis patients and construct artificial neural networks (ANNs) to correctly classify patients into AgP or CP class. The fit of probability distributions on the datasets was tested by the Akaike information criterion (AIC). ANNs were trained by cross entropy (CE) values estimated between probabilities of showing certain levels of immunologic parameters and a reference mode probability proposed by kernel density estimation (KDE). The weight decay regularization parameter of the ANNs was determined by 10-fold cross-validation. Possible evidence for 2 clusters of patients on cross-sectional and longitudinal bone loss measurements were revealed by KDE. Two to 7 clusters were shown on datasets of CD4/CD8 ratio, CD3, monocyte, eosinophil, neutrophil and lymphocyte counts, IL-1, IL-2, IL-4, INF-gamma and TNF-alpha level from monocytes, antibody levels against A. actinomycetemcomitans (A. a.) and P. gingivalis (P. g.). ANNs gave 90%-98% accuracy in classifying patients into either AgP or CP. The best overall prediction was given by an ANN with CE of monocyte, eosinophil, neutrophil counts and CD4/CD8 ratio as inputs. ANNs can be powerful in classifying periodontitis patients into AgP or CP, when fed by CE values based on KDE. Therefore ANNs can be employed for accurate diagnosis of AgP or CP by using relatively simple and conveniently obtained parameters, like leukocyte counts in peripheral blood. This will allow clinicians to better adapt specific treatment protocols for their AgP and CP patients.