Predicting Elevated Natriuretic Peptide in Chest Radiography: Emerging Utilization Gap for Artificial Intelligence

Background Diagnosing heart failure can be challenging, especially for non-cardiovascular specialists. Objectives This study assessed an artificial intelligence (AI) model’s performance in predicting elevated brain natriuretic peptide (BNP) levels from chest radiograms and its potential to enhance human diagnostics. Methods 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 evaluation. The radiograms were labelled by plasma BNP levels >= 200 pg/mL. Models were developed to predict elevated BNP levels. Humans were evaluated to predict elevated BNP levels, followed by the same test, referring to the AI model’s predictions. Results Among the 1607 patients, 10103 images and BNP values were collected. The AI model achieved an accuracy of 0.855, precision of 0.873, sensitivity of 0.827, receiver-operating-characteristics area-under-curve of 0.929. The accuracy of the testing with the 100 images 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 experts was higher than that of non-experts (0.728±0.051 vs. 0.692±0.042, 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 radiograms and has the potential to improve human performance. The gap in utilizing new tools represents one of the emerging issues. Tweet We developed the AI model to predict elevated BNP levels from chest X-rays. The model outperformed cardiologists in predicting elevated BNP levels, and the AI assistance improved the diagnostic performance of both experts and non-experts. #HeartFailure, #CHF, #cvImaging ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement There is no financial support for this study. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB of Hiroshima City Asa Hospital gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The source codes and weights of the models used in this study will be available on GitHub. [https://github.com/ekagawa007/kagawa\_for\_review][1] * AI : artificial intelligence BNP : brain natriuretic peptide ROC : receiver-operating-characteristics PR : precision-recall AUC : area-under-curve SD : standard deviation GPU : graphic processing unit GRAD-CAM : gradient-class activation maps. [1]: https://github.com/ekagawa007/kagawa_for_review