Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks

Purpose: To develop and test an artificial intelligence (AI) system, based on deep convolutional neural networks (CNNs), for automated real- time triaging of adult chest radiographs on the basis of the urgency of imaging appearances. Materials and Methods: An AI system was developed by using 470 388 fully anonymized institutional adult chest radiographs acquired from 2007 to 2017. The free- text radiology reports were preprocessed by using an in- house natural language processing (NLP) system modeling radiologic language. The NLP system analyzed the free- text report to prioritize each radiograph as critical, urgent, nonurgent, or normal. An AI system for computer vision using an ensemble of two deep CNNs was then trained by using labeled radiographs to predict the clinical priority from radiologic appearances only. The system's performance in radiograph prioritization was tested in a simulation by using an independent set of 15 887 radiographs. Prediction performance was assessed with the area under the receiver operating characteristic curve; sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also determined. Nonparametric testing of the improvement in time to final report was determined at a nominal significance level of 5%. Results: Normal chest radiographs were detected by our AI system with a sensitivity of 71%, specificity of 95%, PPV of 73%, and NPV of 94%. The average reporting delay was reduced from 11.2 to 2.7 days for critical imaging findings (P < .001) and from 7.6 to 4.1 days for urgent imaging findings (P < .001) in the simulation compared with historical data. Conclusion: Automated real- time triaging of adult chest radiographs with use of an artificial intelligence system is feasible, with clinically acceptable performance. (C) RSNA, 2019