Prediction of the Development of Periodontal Disease

Abstract

Introduction. Periodontal tissue disease is one of the most common dental pathologies, which among young people occurs with a frequency of 60% to 99%. Therefore, the problem of finding new links in the pathogenesis, the reasons for the growing prevalence of periodontal disease, as well as effective methods for its early diagnosis and prevention, is relevant. Objectives. Establish the possibility of using individual stomatological and psychophysiological features to predict the development of periodontal disease. Materials and methods. 156 students aged 18-23 years old without systemic diseases were surveyed for some features of oral hygiene and nutrition. Also the study subjects underwent a dental examination, psychological testing and the assessment of individual typological features of higher nervous activity and autonomous regulation. The model for statistical prediction were designed using neural networks. Results. Two neural networks were designed with the best predictors among dental history and examination, psychological testing, parameters of higher nervous activity and heart rate variability analysis. The diagnostic sensitivity of the first prognostic model was 83.33 % and the specificity was 92.31 %. The second model was characterized by 90.00 % sensitivity and 78.57 % specificity. Conclusion. The method of modeling using neural networks based on the index assessment of the condition of teeth hard tissues, the level of oral hygiene and the evaluation of psychophysiological features can effectively predict the risk of periodontal disease development in young people

Artificial Intelligence in Dentistry—Narrative Review

“…In dental surgery, neural networks may be helpful in orthognathic surgery planning, prediction of post-extraction complications, bone lesion detection, and differentiation and implantological treatment planning [ 41 , 42 , 43 , 44 , 45 , 46 , 47 ]. Furthermore, artificial intelligence is spreading into periodontology and in the above-mentioned studies, it was used to evaluate the periodontal bone loss, peri-implant bone loss, and to predict the development of periodontitis due to the psychological features [ 49 , 50 , 51 , 52 , 53 , 54 ]. This review shows that artificial intelligence has developed very fast in recent years and it may become an ordinary tool in modern dentistry in the near future.…”

“…They concluded that patients’ level of anxiety and stress hormone levels had an impact on periodontitis ( Table 5 ). Assessment of the condition of teeth hard tissues, the level of oral hygiene, and the evaluation of psychophysiological features with the use of neural networks can effectively predict the risk of periodontal disease development in young people [ 53 ].…”

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

“…In the study of Jun-Young Cha et al, the bone loss percentage was calculated and classified by convolutional neural networks on periapical radiographs and its accuracy was 88.89%. This method can be used to assess the severity of peri-implantitis [ 50 , 51 , 52 ].…”

“…The diagnostic sensitivity of the first prognostic model was 83.33% and the specificity was 92.31%. They concluded that assessment of the condition of teeth hard tissues, the level of oral hygiene, and the evaluation of psychophysiological features can effectively predict the risk of periodontal disease development in young people aged 18–23 [ 51 ].…”

Особливості Мислення У Осіб З Різною Теплочутливістю

“…Відомі дані про вплив типів погоди на розумову працездатність у старшокласників, які свідчать про її зниження із погіршенням погодних умов. Відомо, що за ІІІ типу погоди також погіршуються увага, пам'ять, розумові здібності, зростає латентний період складної зорово-моторної реакції [8,9].…”