Assessment of Relative Risk for Periodontitis Progression Using Neural Network Modeling: Cohort Retrospective Study

Background. Currently accepted risk assessments of periodontitis progression are determinants of indirect stability: periodontal pockets, persistent bleeding of the gums, tooth mobility, local risk factors. In the era of case-oriented medicine, a relevant solution would be to choose periodontal therapy according to one-time consideration of the maximum available range of individual risk factors rather than on general clinical guidelines.

Objectives. The study was aimed at determining the relative risk of periodontitis progression after active basic therapy using neural network modeling.

Methods. A cohort retrospective study was performed on 109 patients of both sexes, aged 30 to 70 years, after basic treatment of chronic periodontitis (mild, moderate and severe) in the period from 1999 to 2016, who were on supportive periodontal therapy (SPT) for 5 years ≤SPT≤ 20 years. The authors considered data from objective examination of the periodontium and categorical indices (24 in total) assessed before treatment, 4–6 months after basic (active) treatment and 5 years ≤SPT≤ 20 years. Following the analysis of descriptive statistics, target quantitative indices were determined for prognostic modeling of treatment outcomes in periodontitis patients and calculating the residual risk of disease progression. Statistical processing of obtained data was carried out using the Statistica 13.3 package (Tibco, USA). Mean values of the indicators at different time points were compared by means of Wilcoxon’s and Signs criteria; Spearman’s rank correlation coefficient was used to evaluate relevance between predictors and target indicators. The level of statistical significance p = 0.05 was accepted in all cases of analysis. DataMining, an automated neural network of Statistica software, was used as a tool to build neural network models. The task of classifying the level of risk of disease progression was solved by means of ROC analysis. The prognostic potential of the model was assessed using sensitivity and specificity measures.

Results. The heterogeneous dynamics of predictor variables describing the state of the periodontium was determined. The outcomes of regenerative periodontal surgery, regardless of gender, age of patients and comorbidities, significantly outperformed those of other approaches, due to the formation of a new dentogingival attachment, although to different extent. Another positive functional outcome was recorded in restoring the dentition integrity by implantation, without any mutually damaging effects. Since revealing the interrelationships between indicators is not equivalent to the predictive value, prognostic models were built for target indicators and stratification of the relative risk of periodontitis progression using automated neural networks. The networks with the best prognostic properties were selected out of 1000 automatically built and trained neural networks — double-layer perceptrons. The sensitivity of the relative risk prognostic model on the training, control and test samples made up 90%, 67%, 80%; the specificity of the model made up 81.481%, 85.714%, 100%. Overall, in the cohort, the sensitivity and specificity accounted for 85.937% and 86.666%. The area under the curve (ROC AUC) is 0.859.

Conclusion. The use of an artificial intelligence algorithm for the construction of neural networks for target predictors and stratification of the relative risk of periodontitis progression has advantages over classical methods — it is instrumental in solving classification and regression problems with categorical and quantitative predictor variables using data of arbitrary nature of large and small volumes. The practical implementation of the study results is reflected in the development of a relative risk calculator based on a written computer program.