Mathematical Model of Neural Network Development for Morphological Assessment of Repair and Remodeling of Bone Defect

• Published in: Mathematical models and computer simulations Vol. 14; no. 2; pp. 281 - 288
• Main Authors: Fedosova, N. V., Berchenko, G. N., Mashoshin, D. V.
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 2022; Springer Nature B.V
• Subjects: Artificial intelligence; Artificial neural networks; Image analysis; Mathematical analysis; Mathematical Modeling and Industrial Mathematics; Mathematical models; Mathematics; Mathematics and Statistics; Medical research; Morphology; Network reliability; Neural networks; Qualitative analysis; Random sampling; Scientists; Simulation and Modeling; Statistical methods; mathematical model; CNN; bone defect healing; GoogLeNet; artificial intelligence
• ISSN: 2070-0482; 2070-0490
• DOI: 10.1134/S2070048222020065

Currently, there is no longer any doubt that the use of artificial intelligence models has exceptional potential in many areas of our life, including medicine. It brings medical research to a fundamentally new qualitative level due to a high degree of accuracy in the analysis of growing volumes of medical data, avoiding the influence of the human factor and related medical mistakes. Despite the rapid development of neural networks, their practical application in modern scientific research is extremely rare. In the articles of scientists, there are no works in which neural networks used for analytics of morphological images. The methods of mathematical statistics currently used for this purpose are very complex and, in most cases, difficult for medical scientists to apply. This leads to many errors and, in some cases, to unscientific and absurd conclusions. Therefore, the authors of this work have developed methodology of creation the mathematical model based on GoogLeNet architecture, which is used for investigation of morphological healing process of a bone defect. The expert pathologist confirms results of morphological investigation conducted by mathematical model created based on a convolutional artificial neural network. The reliability of the results of a qualitative and quantitative morphological study—image analysis using the neural network developed by the authors of the article—significantly exceeds the reliability of the processing of the results by a specialist performed in the traditional way. The mathematical model makes it possible to exclude the random sampling, as well as the human factor in evaluating research results.