Using AI to Identify Chest Radiographs with No Actionable Disease in Outpatient Imaging

Abstract

Abstract Background: Chest radiographs are one of the most frequently performed imaging examinations in radiology. Chest radiograph reading is characterized by a high volume of cases, leading to long worklists. However, a substantial percentage of chest radiographs in outpatient imaging are without actionable findings. Identifying these cases could lead to numerous workflow efficiency improvements. Objective: To assess the performance of an AI system to identify chest radiographs with no actionable disease (NAD) in an outpatient imaging population in the United States. Materials and Methods: The study includes a random sample of 15,000 patients with chest radiographs in posterior-anterior (PA) and optional lateral projections from an outpatient imaging center with multiple locations in the Northeast United States. The ground truth was established by manually reviewing procedure reports and classifying cases as non-actionable disease (NAD) or actionable disease (AD) based on predetermined criteria. The NAD cases include both completely normal chest radiographs without any abnormal findings and radiographs with non-actionable findings. The AI NAD Analyzer1 trained on more than 1.3 million radiographs provides a binary case level output for the chest radiographs as either NAD or potential actionable disease (PAD). Two systems A (more specific) and B (more sensitive) were trained. Both systems were capable of processing either frontal only or frontal-lateral pair. Results: After excluding patients < 18 years (n=861) as well as the cases not meeting the image quality requirements of the AI NAD Analyzer (n=82), 14057 cases (average age 56±16.1 years, 7722 women and 6328 men) remained for the analysis. The AI NAD Analyzer with input consisting of PA and lateral images, correctly classified 2891 cases as NAD with concordance between ground truth and AI, which is 20.6% of all cases and 29.1% of all ground truth NAD cases. The miss rate was 0.3% and included 0.06% significant findings. With a more specific version of the AI NAD Analyzer (System A), there were 12.2% of all NAD cases were identified correctly with a miss rate of 0.1%. No cases with critical findings were missed by either system. Conclusion: The AI system can identify a meaningful number of chest radiographs with no actionable disease in an outpatient imaging population with a very low rate of missed findings. 1For research purposes only. Not for clinical use. Future commercial availability cannot be guaranteed.