Development the mathematical model neural network for morphological assessment of repair and remodeling of bone defect

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 used for morphological healing process of a bone defect investigation. 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.