The use of artificial intelligence technologies as a way to ensure the quality of chest radiography.

When performing radiographic studies, errors may occur that reduce the diagnostic value of the radiographs and complicate their interpretation by radiologists and diagnostic software based on artificial intelligence technology. The creation of automated quality assessment systems will optimize this process, especially in conditions of increased workload of medical personnel. Purpose: development of an automated quality control tool for chest radiographs, which allows for quality control of the patient’s positioning and the correctness of filling in meta-information about the study. Material and methods.To train and test automated quality control models, were used 61505 chest radiographs, obtained from open datasets and the Unified Radiological Information Service of the Unified Medical Information Analysis System of the City of Moscow. To create models we used transfer training of deep neural network architectures VGG19 and ResNet152V2. Results. 7 models were created: a model for determining the anatomical area of study, a model for determining projection, a model for determining photometric interpretation, models for determining incomplete visualization of the anatomical area on the frontal and lateral projections of the chest radiographs, a model for determining rotation on the lateral projection of the chest radiographs. All created models have diagnostic accuracy metrics above 95%, which allows them to be used in clinical practice. Based on the developed models, a web-based quality control tool of the chest radiographs was created, which allows analyzing the quality of X-ray datasets. Conclusion. The active use of this quality control tool will optimize the process of assessing the quality of diagnostic studies and facilitate the processes of classification of studies and the formation of datasets. Also, this tool can be used to support the decision-making of an X-ray technician and assess the quality of the study before sending the study for processing to artificial intelligence-based services.