Evaluation of the Progression of Periodontitis with the Use of Neural Networks

Abstract

Periodontitis is an inflammatory disease of the tissues surrounding the tooth that results in loss of periodontal attachment detected as clinical attachment loss (CAL). The mildest form of periodontal disease is gingivitis, which is a necessary condition for periodontitis development. We can distinguish also some modifying factors which have an influence on the rate of development of periodontitis from which the most important are smoking and poorly controlled diabetes. According to the new classification from 2017, we can identify four stages of periodontitis and three grades of periodontitis. Grades tell us about the periodontitis progression risk and may be helpful in treatment planning and motivating the patients. Artificial neural networks (ANN) are widely used in medicine and in dentistry as an additional tool to support clinicians in their work. In this paper, ANN was used to assess grades of periodontitis in the group of patients. Gender, age, nicotinism approximal plaque index (API), bleeding on probing (BoP), clinical attachment loss (CAL), and pocket depth (PD) were taken into consideration. There were no statistically significant differences in the clinical periodontal assessment in relation to the neural network assessment. Based on the definition of the sensitivity and specificity in medicine we obtained 85.7% and 80.0% as a correctly diagnosed and excluded disease, respectively. The quality of the neural network, defined as the percentage of correctly classified patients according to the grade of periodontitis was 84.2% for the training set. The percentage of incorrectly classified patients according to the grade of periodontitis was 15.8%. Artificial neural networks may be useful tool in everyday dental practice to assess the risk of periodontitis development however more studies are needed.

The PocketPerio application significantly increases the accuracy of diagnosing periodontal conditions in didactic and chairside settings

“…This suggestion reflects an emerging use of machine-learning-assisted approaches in the daily teaching process. Artificial intelligence (AI) and machine-learning-assisted approaches have been continuously implicated in dentistry to detect a wide variety of radiographic findings 37,38 , perform and analyze immune profiling to assist with the diagnosis of peri-implantitis 39 , enhance the interdisciplinary interaction between medical and dental professionals 40 , and assist with diagnosis of periodontitis 41 . Nevertheless, limitations in study design and the associated high risk of bias make the use of these applications in dental practice challenging 42,43 .…”

Progression of Selected Parameters of the Clinical Profile of Patients with Periodontitis Using Kohonen’s Self-Organizing Maps

“…Nowadays, the disciplines of medicine and dentistry are becoming more and more dependent on artificial intelligence (AI) [ 15 , 21 ]. It can be helpful in a range of circumstances when new technology could be advantageous and helpful to people.…”

“…The methodology that we have employed allows us to notice some of variables which present statistically significant differences in terms of the probability of progression. The dependences of these magnitudes do not appear when a customary statistics method comparing the means between the several groups is undertaken [ 21 ].…”

Dry Weight Prediction of Wedelia trilobata and Wedelia chinensis by Using Artificial Neural Network and MultipleLinear Regression Models

“…Sensitivity analysis was performed by choosing the most appropriate input parameters that control the dry weight of WT and WC within submergence and eutrophication, after determining the best ANN model. The sensitivity analysis was performed by running a dataset without any input variables, and the values of R 2 , RMSE, and MAE determined the accuracy of the model [37].…”