UvA-DARE ( Digital Academic Repository ) Artificial neural networks for the diagnosis of aggressive periodontitis trained by immunologic parameters

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 crossvalidation. 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-c and TNF-a 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. Citation: Papantonopoulos G, Takahashi K, Bountis T, Loos BG (2014) Artificial Neural Networks for the Diagnosis of Aggressive Periodontitis Trained by Immunologic Parameters. PLoS ONE 9(3): e89757. doi:10.1371/journal.pone. 0089757 Editor: Ali Al-Ahmad, University Hospital of the Albert-Ludwigs-University Freiburg, Germany Received November 27, 2013; Accepted January 23, 2014; Published March 6, 2014 Copyright: 2014 Papantonopoulos et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The Academic Center for Dentistry Amsterdam (ACTA) (author B.G.L.) is supported in part by a grant from the University of Amsterdam for research into the focal point ‘‘Oral infections and Inflammation’’. This research has been co-financed by the European Union (European Social Fund, ESF) and Greek national funds through the Operational Program ‘‘Education and Lifelong Learning’’ of the National Strategic Reference Framework (NSRF) – Research Funding Program: THALES: Investing in knowledge society through the European Social Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: ppntnpls@otenet.gr