Predicting elevated natriuretic peptide in chest radiography: emerging utilization gap for artificial intelligence

Abstract

Aims This study assessed an artificial intelligence (AI) model's performance in predicting elevated brain natriuretic peptide (BNP) levels from chest radiograms and its effect on diagnostic performance among healthcare professionals. Methods and results Patients who underwent chest radiography and BNP testing on the same day were included. Data were sourced from two hospitals: one for model development, and the other for external testing. Two final ensemble models were developed to predict elevated BNP levels of ≥ 200 pg/mL and ≥ 100 pg/mL, respectively. Humans were evaluated to predict elevated BNP levels, followed by the same test, referring to the AI model's predictions. A total of 8390 images were collected for model creation, and 1713 images, for tests. The AI model achieved an accuracy of 0.855, precision of 0.873, sensitivity of 0.827, specificity of 0.882, f1 score of 0.850, and receiver-operating-characteristics area-under-curve of 0.929. The accuracy of the testing by 35 participants significantly improved from 0.708 ± 0.049 to 0.829 ± 0.069 (P < 0.001) with the AI assistance (an accuracy of 0.920). Without the AI assistance, the accuracy of the veterans in the medical career was higher than that of early-career professionals (0.728 ± 0.051 vs. 0.692 ± 0.042, P = 0.030); however, with the AI assistance, the accuracy of the early-career professionals was rather higher than that of the veterans (0.851 ± 0.074 vs. 0.803 ± 0.054, P = 0.033). Conclusion The AI model can predict elevated BNP levels from chest radiograms and has the potential to improve human performance. The gap in utilizing new tools represents one of the emerging issues.Lay summary Heart failure is a growing medical issue, and there is a high demand for automated tools to support daily medical practice. We developed an artificial intelligence (AI) model that can predict heart failure by identifying elevated biomarkers from chest X-ray images. Our results showed that this AI model performed better than expert cardiologists in predicting these biomar-kers. In this study, healthcare providers, including both those early in their careers and seasoned veterans, were assessed on their ability to detect these biomarkers from chest radiograms. The AI model significantly improved diagnostic accuracy for both groups, with early-career professionals performing as well or better than the veterans. The study highlights how the AI model enhances healthcare providers' capabilities, with varying degrees of improvement among individuals. The AI model promises to support daily medical practice and elevate the quality of heart failure management. As the adoption of innovative tools like AI becomes more crucial, addressing the gap in their utilization is an emerging issue. We must embrace and adapt to new ideas, technologies, and methods to advance medical care.