Abstract 11601: Does Artificial Intelligence Dream of Elevated Natriuretic Peptide in Chest Radiography?

Abstract

Introduction: Heart failure pandemic is a medical issue, and its diagnosis can be challenging. Hypothesis: To assess the hypothesis that the artificial intelligence (AI) model for predicting elevated brain natriuretic peptide (BNP) levels from chest X-ray images has sufficient performance and could improve the diagnostic performance of human. Methods: Patients who underwent chest radiography and BNP testing on the same day were enrolled in this study. The chest X-ray images were labeled based on plasma BNP levels >= 200 pg/mL. We trained models that predicted the label, and the final ensemble model was constructed. Humans were trained and tested to predict labels, followed by the same test, referring to the prediction of the AI model. Diagnostic performance was assessed. Results: Among the 1607 patients, 10103 chest X-ray images and BNP values were collected. We assembled 31 models as the final model with an accuracy of 0.855, precision of 0.873, recall of 0.827, receiver-operating-characteristics area-under-curve (AUC) of 0.929, and precision-recall AUC of 0.934. The accuracy of the testing by 35 individuals significantly improved from 0.708±0.049 to 0.829±0.069 (P < 0.001) with AI assistance. The accuracy of the experts was higher than that of non-experts (0.692±0.042 vs. 0.728±0.051, P = 0.030); however, with the AI assistance, the accuracy of the non-experts was rather higher than that of the experts (0.851±0.074 vs. 0.803±0.054, P = 0.033). Conclusions: The AI model can predict elevated BNP levels from chest X-ray images and could improve the diagnostic performance of humans.