Abstract
In the diagnostic practice of radiology, doctors visually evaluate medical images to identify characteristics and monitor disease. AI and computer vision techniques can recognize complex patterns in imaging data and provide quantitative estimates of radiographic characteristics. Based on the methodology developed by the authors, results were obtained on the interpretation and search for information. This contributes to the rapid processing of the data array and the formation of programs to support decision-making by the radiologist. The applied computational technique is aimed not only at improving the image quality, but also at the ability to search for the CT pattern of more delayed changes. Quantitative analysis of medical imaging data using modern software provides more information than standard X-ray analysis by a radiologist. Research idea: isolation and formation of markers in the area of interest in the structure of the lungs to assess individual characteristics. This provides a more reliable interpretation of the identified changes, with an increase in the accuracy of the diagnosis of respiratory diseases. An increase in accuracy is necessary to identify pathological structures, accelerate the process of diagnosing respiratory diseases, and reduce the proportion of repeated examinations. The following results were obtained: a method was developed for the analysis and interpretation of pathological changes in the lungs to improve the quality and accuracy of X-ray diagnostics; a search for reference images was performed to form a prediction of delayed events and compare the results in a group of patients; a comparative analysis of the data obtained using the computational technique has been carried out. The study was carried out within the framework of the grant «Methods of artificial intelligence and computer vision to improve the accuracy of remote diagnostics of respiratory diseases in the northern group of regions of the Krasnoyarsk Territory» with financial support from the Krasnoyarsk Regional Fund for the Support of Scientific and Scientific and Technical Activities.
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