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To investigate the feasibility of automated follow-up recommendations based on findings in radiology reports, this paper proposed a Natural Language Processing model specific for Pulmonary Nodule Radiology Reports. Unstructured findings used to describe pulmonary nodules in 48,091 radiology reports were processed in this study. We established an NLP model to extract information entities from findings of radiology reports, using deep learning and conditional random-field algorithms. Subsequently, we constructed a knowledge graph comprising 168 entities and four relationships, based on the export recommendations of the internationally renowned Fleischner Society for pulmonary nodules. These were employed in combination with rule templates to automatically generate follow-up recommendations. The automatically generated recommendations were then compared to the impression part of the reports to evaluate the matching rate of proper follow ups in the current situation. The NLP model identified eight types of entities with a recognition accuracy of up to 94.22%. A total of 43,898 out of 48,091 clinical reports were judged to contain appropriate follow-up recommendations, corresponding to the matching rate of 91.28%. The results show that NLP can be used on Chinese radiology reports to extract structured information at the content level, thereby realizing the prompt and intelligent follow-up suggestion generation or post-quality management of follow-up recommendations. [ABSTRACT FROM AUTHOR]

Radiographic testing is generally used in the quality management of aeroengine turbine blades. Traditional radiographic testing is critically dependent on artificially detecting professional inspectors. Thus, it sometimes tends to be error-prone and time-consuming. In this study, we gave an automatic defect detection method by combining radiographic testing with computer vision. A defect detection algorithm named DBFF-YOLOv4 was introduced for X-ray images of aeroengine turbine blades by employing two backbones to extract hierarchical defect features. In addition, a new concatenation form containing all feature maps was developed which play an important role in the present defect detection framework. Finally, a defect detection and recognition system was established for testing and output of complete turbine blade X-ray images. Meanwhile, nine cropping cycles for one defect, flipping, brightness increasing and decreasing were applied for expansion of training samples and data augmentation. The results found that this defect detection system can obtain a recall rate of 91.87%, a precision rate of 96.7%, and a false detection rate of 7% within the score threshold of 0.5. It was proven that cropping nine times and data augmentation are extremely helpful in improving detection accuracy. This study provides a new way of automatic radiographic testing for turbine blades. [ABSTRACT FROM AUTHOR]