Possibilities and limitations of using machine text processing tools in Russian radiology

Backgraund: In radiology, important information is contained not only in medical images, but also in the accompanying text descriptions created by radiologists. Identification of study protocols containing certain data and extraction of these data can be useful primarily for clinical problems, however, given the large amount of such data, the development of machine analysis algorithms is necessary. 
Aim: to estimate the possibilities and limitations of using of tool for machine processing of radiology reports to search for pathological findings. 
Material and Methods: to create an algorithm for automatic analysis of radiology reports, use cases were selected that participated in the Experiment on the use of innovative technologies in the computer vision for the analysis of medical images in 2020. The use cases included mammography, chest X-ray, chest CT and LDCT, performed in Moscow. A dictionary of keywords has been compiled. After the automatic marking of the reports by the developed tool, the results were assessed by a radiologist. The number of protocols analyzed by the radiologist was 977 for mammography, 4804 for all chest X-ray scans, 4074 for chest CT and 398 chest LDCT. 
Results: The best results were achieved in the search for viral pneumonia in chest CT reports (0.993), breast cancer in mammography reports (0.996). The accuracy of searching for signs of lung cancer in chest CT and LDCT, pathological findings in the chest X-ray reports was 0.972 and 0.982. 
Conclusions: Machine methods with high accuracy can be used to automatically classify the radiology reports of mammography, chest CT with viral pneumonia. To search for signs of lung cancer in chest CT and LDCT, pathological findings in chest X-ray, the achieved accuracy is sufficient for successful application to automatically compare the conclusions of physicians and artificial intelligence models.