Artificial neural networks for the diagnosis of aggressive periodontitis trained by immunologic parameters

PLoS ONE

Volumn 9, Issue 3, 2014, Pages

  Papantonopoulos, Georgios ^a   Takahashi, Keiso ^b   Bountis, Tasos ^a   Loos, Bruno G ^c  

^a UNIVERSITY OF PATRAS   (Greece)

^b OHU UNIVERSITY   (Japan)

^c UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

BACTERIUM ANTIBODY; CD3 ANTIGEN; GAMMA INTERFERON; INTERLEUKIN 1; INTERLEUKIN 2; INTERLEUKIN 4; TUMOR NECROSIS FACTOR ALPHA;

ADULT; AGGREGATIBACTER ACTINOMYCETEMCOMITANS; AGGRESSIVE PERIODONTITIS; ALVEOLAR BONE LOSS; ARTICLE; ARTIFICIAL NEURAL NETWORK; CD4 CD8 RATIO; CHRONIC PERIODONTITIS; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIFFERENTIAL DIAGNOSIS; DISEASE CLASSIFICATION; ENTROPY; EOSINOPHIL COUNT; HUMAN; KERNEL METHOD; LYMPHOCYTE COUNT; MAJOR CLINICAL STUDY; MONOCYTE; NEUTROPHIL COUNT; PORPHYROMONAS GINGIVALIS; ALGORITHM; BACTERIAL INFECTION; COMPUTER ASSISTED DIAGNOSIS; HOST PATHOGEN INTERACTION; IMMUNOLOGY; LEUKOCYTE COUNT; METABOLISM; MICROBIOLOGY; PHYSIOLOGY; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

AGGREGATIBACTER ACTINOMYCETEMCOMITANS; AGGRESSIVE PERIODONTITIS; ALGORITHMS; ANTIBODIES, BACTERIAL; ANTIGENS, CD3; BACTERIAL INFECTIONS; CD4-CD8 RATIO; CHRONIC PERIODONTITIS; DIAGNOSIS, COMPUTER-ASSISTED; DIAGNOSIS, DIFFERENTIAL; HOST-PATHOGEN INTERACTIONS; HUMANS; INTERFERON-GAMMA; INTERLEUKIN-1; INTERLEUKIN-2; INTERLEUKIN-4; LEUKOCYTE COUNT; MONOCYTES; NEURAL NETWORKS (COMPUTER); PORPHYROMONAS GINGIVALIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TUMOR NECROSIS FACTOR-ALPHA;

EID: 84897385758     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0089757     Document Type: Article

References (39)

1. Kinane DF, Berglund T, Lindhe J (2008) Pathogenesis of periodontitis. In: Lindhe J, Karring T, Lang NP, editors. Clinical Periodontology and Implant Dentistry. Blackwell Munksgaard: Oxford. pp. 285-306.
2. Eke PI, Dye BA, Wei L, Thornton-Evans GO, Genco RJ (2012) Prevalence of periodontitis in adults in the United States: 2009 and 2010. J Dent Res 91:914-920.
3. Laine ML, Crielaard W, Loos BG (2012) Genetic susceptibility to periodontitis. Periodontol 2000 58:37-68.
4. Nicolis G, Nicolis C (2012) Foundations of complex systems: emergence, information and prediction. World Scientific: Singapore. 12 p.
5. Armitage GC (1999) Developement of a classification system for periodontal diseases and conditions. Ann Periodontol 4:1-6.
6. Armitage GC (2013) Learned and unlearned concepts in periodontal diagnostics: a 50-year perspective. Periodontol 2000 62:20-36.
7. Tonetti MS, Mombelli A (2008) Aggressive periodontitis. In: Lindhe J, Karring T, Lang NP, editors. Clinical Periodontology and Implant Dentistry. Blackwell Munksgaard: Oxford. pp. 428-458.
8. Armitage GC, Cullinan MP (2010) Comparison of the clinical features of chronic and aggressive periodontitis. Periodontol 2000 53:12-27.
9. Papantonopoulos G, Takahashi K, Bountis T, Loos BG (2013a) Using cellular automata experiments to model periodontitis: A first step towards understanding the nonlinear dynamics of the disease. Int J Bifurcation Chaos 23:1350056.
10. Papantonopoulos G, Takahashi K, Bountis T, Loos BG (2013b) Mathematical modeling suggests periodontitis behaves as a nonlinear chaotic dynamical process. J Periodontol 84(10):e29-e39.
11. Silverman BW (1986) Density estimation for statistics and data analysis. Chapman and Hall: London. 6 p.
12. Simonoff JS (1996) Smoothing methods in statistics. New York: Springer-Verlag: New York. 13 p.
13. Sheather SJ (2004) Density estimation. Stat Science 19:588-597.
14. Abdi H (2003) Neural networks. In: Lewis-Beck M, Bryman A, Futing T, editors. Encyclopaedia of Social Sciences Research Methods. Sage: Thousand Oaks (CA). pp. 725-729.
15. Papantonopoulos G (2004) Effect of periodontal therapy in smokers and nonsmokers with advanced periodontal disease: Results after maintenance therapy for a minimum of 5 years. J Periodontol 75:839-843.
16. Loos BG, Craandijk J, Hoek FJ, Wertheim-van Dillen PME, van der Velden U (2000) Elevation of systemic markers related to cardiovascular diseases in the peripheral blood of periodontitis patients. J Periodontol 71:1528-1534.
17. Loos BG, Roos MT, Schellekens PT, van der Velden U, Miedema F (2004) Lymphocyte number and function in relation to periodontitis and smoking. J Periodontol 75:557-564. (Pubitemid 38635025)
18. Graswinckel JEM, van der Velden U,van Winkelhoff AJ, Hoek FJ, Loos BG (2004) Plasma antibody levels in periodontitis patients and controls. J Clin Periodontol 31:562-568. (Pubitemid 38901227)
19. Takahashi K, Ohyama H, Kitanaka M, Sawa T, Mineshiba J, et al. (2001) Heterogeneity of host immunological risk factors in patients with aggressive periodontitis. J Periodontol 72:425-437. (Pubitemid 33664488)
20. Kinane DF (2001) Periodontal disease in children and adolescents: introduction and classification. Periodontol 2000 26:7-15.
21. Hall P, Kang KH (2005) Bandwidth choice for nonparametric classification. Ann Statist 33:284-306. (Pubitemid 40653791)
22. Vose D (2008) Risk analysis: A quantitative guide. West Sussex: John Wiley & Sons. 115 p.
23. Sakamoto Y, Ishikuro M, Kitaqawa G (1986) Akaike information criterion statistics. Springer-Verlag: New York. 56 p.
24. Myung J (2003) Tutorial on maximum likelihood estimation. J Math Psychol 47:90-100.
25. Burnham KP, Anderson DR (2004) Multimodel inference: understanding AIC and BIC in model selection. Sociol Methods Res 33:261-304. (Pubitemid 39519124)
26. de Boer PT, Kroese DP, Mannor S, Rubinstein RY (2005) A tutorial on the cross-entropy method. Ann Oper Res 134:18-67.
27. Neal R, Zhang J (2006) High dimensional classification with bayesian neural networks and dirichlet diffusion trees. In: Guyon I, Gunn S, Nikravesh M, Zadeh L, editors. Feature Extraction, Foundations and Applications. Springer: New York. pp. 265-296.
28. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: Data mining, inference, and prediction. Springer: New York. 389 p.
29. Zhang GP (2000) Neural networks for classification: A survey. IEEE Trans Syst Man Cybern 30:451-462.
30. Mazurowski MA, Habas PA, Zurada JM, Lo JY, Baker JA, et al. (2008) Training neural network classifiers for medical decision making; The effects of imbalanced datasets on classification performance. Neural Netw 21:427-436.
31. Shi H-Y, Lee K-T, Lee H-H, Ho W-H, Sun D-P, et al. (2012) Comparison of artificial neural network and logistic regression models for predicting in-hospital mortality after primary liver cancer surgery. PLOS ONE 7(4):e35781.
32. Offenbacher S, Barros SP, Singer RE, Moss K, Williams RC, et al. (2007) Periodontal disease at the biofilm-gingival interface. J Periodontol 78:1911-1925.
33. Teles RP, Gursky LC, Faveri M, Rosa EA, Teles FRF, et al. (2010) Relationships between subgingival microbiota and GCF biomarkers in generalized aggressive periodontitis. J Clin Periodontol 37: 313-323.
34. Razzouk S, Termechi O (2013) Host genome, epigenome, and oral microbiome interactions: towards personalized periodontal therapy. J Periodontol 84:1266-1271.
35. Loos BG, Tjoa S (2005) Host-derived diagnostic markers for periodontitis: do they exist in gingival crevicular fluid? Periodontol 2000 39:53-72. (Pubitemid 43026179)
36. Bartold PM, Van Dyke E (2013) Periodontitis: a host-mediated disruption of microbial homeostasis. Unlearning learned concepts. Periodontol 2000 62:203-217.
37. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157-1182.
38. Van der Velden U (2005) Purpose and problems of periodontal disease classification. Periodontol 2000 39:13-21.
39. Kebschull M, Guarnieri P, Demmer RT, Boulesteix AL, Pavlidis P, et al. (2013) Molecular Differences between Chronic and Aggressive Periodontitis. J Dent Res 92:1081-1088.