A deep learning–based automatic analysis of cardiovascular borders on chest radiographs of valvular heart disease: development/external validation-Reference-Cited by-同舟云学术

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A deep learning–based automatic analysis of cardiovascular borders on chest radiographs of valvular heart disease: development/external validation

Published:2021-10-13 Issue: Volume: Page:
ISSN:0938-7994
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

Kim Cherry,Lee Gaeun,Oh Hongmin,Jeong Gyujun,Kim Sun Won,Chun Eun Ju,Kim Young-Hak,Lee June-Goo,Yang Dong Hyun^ORCID

Funder

the korea health technology r&d project through the korea health industry development institute (khidi), funded by the ministry of health & welfare

national research foundation of korea

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s00330-021-08296-9.pdf

Reference30 articles.

1. Zipes DP (2018) Braunwald's heart disease: a textbook of cardiovascular medicine, 11th Edition. BMH Medical Journal - ISSN 2348–392X:63%V 65

2. Dimopoulos K, Giannakoulas G, Bendayan I et al (2013) Cardiothoracic ratio from postero-anterior chest radiographs: a simple, reproducible and independent marker of disease severity and outcome in adults with congenital heart disease. Int J Cardiol 166:453–457

3. Browne RF, O’Reilly G, McInerney D (2004) Extraction of the two-dimensional cardiothoracic ratio from digital PA chest radiographs: correlation with cardiac function and the traditional cardiothoracic ratio. J Digit Imaging 17:120–123

4. Danzer CS (1919) The cardiothoracic ratio: an index of cardiac enlargement.:Bibliography. The American Journal of the Medical Sciences (1827–1924) 157:513

5. Wittenborg MH, Neuhauser EB (1955) Diagnostic roentgenology in congenital heart disease. Circulation 11:462–485

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