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

Overview

Journal Eur Heart J Imaging Methods Pract

Specialty Cardiology & Vascular Diseases

Date 2024 Oct 15

PMID 39403705

Authors

Eisuke Kagawa

Masaya Kato

Noboru Oda

Eiji Kunita

Michiaki Nagai

Aya Yamane

Shogo Matsui

Yuki Yoshitomi

Hiroto Shimajiri

Tatsuya Hirokawa

Shunsuke Ishida

Genki Kurimoto

Keigo Dote

Affiliations

Soon will be listed here.

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 ( < 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, = 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, = 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.

References

Stevenson L, PERLOFF J . The limited reliability of physical signs for estimating hemodynamics in chronic heart failure. JAMA. 1989; 261(6):884-8. View

Matsumoto T, Kodera S, Shinohara H, Ieki H, Yamaguchi T, Higashikuni Y . Diagnosing Heart Failure from Chest X-Ray Images Using Deep Learning. Int Heart J. 2020; 61(4):781-786. DOI: 10.1536/ihj.19-714. View

Deo R . Machine Learning in Medicine. Circulation. 2015; 132(20):1920-30. PMC: 5831252. DOI: 10.1161/CIRCULATIONAHA.115.001593. View

Gessert N, Lutz M, Heyder M, Latus S, Leistner D, Abdelwahed Y . Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks. IEEE Trans Med Imaging. 2018; 38(2):426-434. DOI: 10.1109/TMI.2018.2865659. View

McLellan J, Bankhead C, Oke J, Hobbs F, Taylor C, Perera R . Natriuretic peptide-guided treatment for heart failure: a systematic review and meta-analysis. BMJ Evid Based Med. 2019; 25(1):33-37. PMC: 7029248. DOI: 10.1136/bmjebm-2019-111208. View

Mueller C, Scholer A, Laule-Kilian K, Martina B, Schindler C, Buser P . Use of B-type natriuretic peptide in the evaluation and management of acute dyspnea. N Engl J Med. 2004; 350(7):647-54. DOI: 10.1056/NEJMoa031681. View

Schulz D, Rasch S, Heilmaier M, Abbassi R, Poszler A, Ulrich J . A deep learning model enables accurate prediction and quantification of pulmonary edema from chest X-rays. Crit Care. 2023; 27(1):201. PMC: 10214619. DOI: 10.1186/s13054-023-04426-5. View

Qin X, Zhang W, Hu X, Zhou W . A deep learning model to identify the fluid overload status in critically ill patients based on chest X-ray images. Pol Arch Intern Med. 2023; 133(2). DOI: 10.20452/pamw.16396. View

Attia Z, Noseworthy P, Lopez-Jimenez F, Asirvatham S, Deshmukh A, Gersh B . An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019; 394(10201):861-867. DOI: 10.1016/S0140-6736(19)31721-0. View

10.

Chakko S, Woska D, Martinez H, de Marchena E, Futterman L, Kessler K . Clinical, radiographic, and hemodynamic correlations in chronic congestive heart failure: conflicting results may lead to inappropriate care. Am J Med. 1991; 90(3):353-9. DOI: 10.1016/0002-9343(91)80016-f. View

11.

Kusunose K, Hirata Y, Tsuji T, Kotoku J, Sata M . Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest X ray. Sci Rep. 2020; 10(1):19311. PMC: 7672097. DOI: 10.1038/s41598-020-76359-w. View

12.

Heidenreich P, Bozkurt B, Aguilar D, Allen L, Byun J, Colvin M . 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2022; 145(18):e895-e1032. DOI: 10.1161/CIR.0000000000001063. View

13.

Tsutamoto T, Wada A, Sakai H, Ishikawa C, Tanaka T, Hayashi M . Relationship between renal function and plasma brain natriuretic peptide in patients with heart failure. J Am Coll Cardiol. 2006; 47(3):582-6. DOI: 10.1016/j.jacc.2005.10.038. View

14.

Zou X, Ren Y, Feng D, He X, Guo Y, Yang H . A promising approach for screening pulmonary hypertension based on frontal chest radiographs using deep learning: A retrospective study. PLoS One. 2020; 15(7):e0236378. PMC: 7380616. DOI: 10.1371/journal.pone.0236378. View

15.

Berg E, Tasken A, Nordal T, Grenne B, Espeland T, Kirkeby-Garstad I . Fully automatic estimation of global left ventricular systolic function using deep learning in transoesophageal echocardiography. Eur Heart J Imaging Methods Pract. 2024; 1(1):qyad007. PMC: 11195714. DOI: 10.1093/ehjimp/qyad007. View

16.

Kusunose K, Hirata Y, Yamaguchi N, Kosaka Y, Tsuji T, Kotoku J . Deep Learning for Detection of Exercise-Induced Pulmonary Hypertension Using Chest X-Ray Images. Front Cardiovasc Med. 2022; 9:891703. PMC: 9240342. DOI: 10.3389/fcvm.2022.891703. View

17.

Hirata Y, Kusunose K, Tsuji T, Fujimori K, Kotoku J, Sata M . Deep Learning for Detection of Elevated Pulmonary Artery Wedge Pressure Using Standard Chest X-Ray. Can J Cardiol. 2021; 37(8):1198-1206. DOI: 10.1016/j.cjca.2021.02.007. View

18.

Maisel A, Krishnaswamy P, Nowak R, McCord J, Hollander J, Duc P . Rapid measurement of B-type natriuretic peptide in the emergency diagnosis of heart failure. N Engl J Med. 2002; 347(3):161-7. DOI: 10.1056/NEJMoa020233. View