Abstract
In this paper, the authors consider how to label and save a large number of images that should be predicted in a single file. The technique of automatic labeling the data set with the finite element model for training of artificial neural network in tomography are proposed. A simple transparent example of thirty-two images to be predicted in a single HDF5 file training of artificial neural network in tomography show accuracy 100% for training set as well for the test set. Then this technique is able to build an information model of salivary immune and periodontal status and to evaluate the correlation between salivary immunoglobulin level, inflammation in periodontal tissues and orthodontic pathology. For this study, patients were divided into the following groups: 76 patients with chronic gingivitis and atopic diseases (group 1), among which the proposed treatment was used; 50 patients without clinical signs of gingivitis with atopic diseases (group 2) for which specific prevention was prescribed; 30 patients with chronic gingivitis and atopic diseases to which the standard treatment of gingivitis has been applied (group 3); 30 patients without clinical signs of gingivitis with atopic diseases using traditional preventive measures (group 4); 35 patients made control group with intact periodontal tissues without somatic pathology (group 5). This study was conducted to assess the state of lipid peroxidation in the oral liquid and the periodontal disease and to detect the correlation between the level of antioxidants in children and inflammation in periodontal tissues by means of regression analyses. The results showed changes in the antioxidant balance in children with atopy that were expressed in an increase in malondialdehyde level, a decrease in superoxide dismutase activity, and a level of reduced glutathione. These indicators can be considered as biological markers of the development of gingivitis at the preclinical stage in children against atopic diseases.
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Bezruk, V.M., Krivenko, S.A., Kryvenko, L.S. (2021). Galois Field Augmentation Model for Training of Artificial Neural Network in Dentistry. In: Radivilova, T., Ageyev, D., Kryvinska, N. (eds) Data-Centric Business and Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-030-43070-2_16
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