문의하기 아이콘
문의하기 텍스트
top 아이콘
coreline logo
close icon
Cookie Settings Information

When you visit our website, we store cookies on your browser to collect information. The information collected may relate to you, your device, or your preferences, and is primarily used to ensure the website functions properly and to provide a more personalized web experience.

However, you may choose to disallow certain types of cookies, which could affect your user experience and the services we are able to offer. You can click on each category below to learn more and adjust your default settings.

Please note that Strictly Necessary Cookies are essential for the basic functioning of the website and cannot be disabled (e.g., maintaining login sessions, remembering settings). For more detailed information about cookies, please refer to our [Privacy Policy].

Manage Consent Preferences
+Strictly Necessary CookiesAlways Active
These cookies are essential for the website to function properly and cannot be switched off in our systems. They are usually set only in response to actions you take, such as setting your privacy preferences, logging in, or filling out forms. You can set your browser to block or alert you about these cookies, but some parts of the site may not function properly as a result. These cookies do not store any personally identifiable information.
+Targeting Cookies
These cookies may be set through our site by our advertising partners. They are used to build a profile of your interests and show you relevant advertisements on other sites. These cookies do not directly store personal information but operate based on unique identification of your browser and device. If you do not allow these cookies, you will experience less targeted advertising.
+Performance
These cookies allow us to aggregate the number of visitors and traffic sources in order to measure and improve the performance of our website. They help us understand which pages are the most popular and how visitors navigate through the site. All information collected is aggregated and therefore anonymous. If you do not allow these cookies, we will not be able to monitor the performance of our site or know when you have visited it.
Save My Choices

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

Authors
Cherry Kim, Gaeun Lee, Hongmin Oh, Gyujun Jeong, Sun Won Kim, Eun Ju Chun, Young‑Hak Kim, June‑Goo Lee, Dong Hyun Yang
Journal
European Radiology
Related Product

Others

Date Published
2021.10
Summary

This study presents the development and validation of a deep learning-based automatic cardiovascular border (CB) analysis algorithm (CB_auto) for diagnosing and quantitatively evaluating valvular heart disease (VHD) using posterior-anterior chest radiographs (CXR). The CB_auto, developed using 1614 normal and VHD CXRs, demonstrated excellent reliability with an intraclass correlation coefficient above 0.98. When compared to manual CB drawing (CB_hand), the absolute percentage measurement error was under 10% for all parameters except for carinal angle and left atrial appendage. In addition, CB parameters were found to be significantly greater in VHD than in normal controls, and correlated significantly with echocardiographic measurements. This suggests that CB_auto could serve as a reliable tool for daily clinical practice and research purposes, enhancing diagnosis and evaluation of VHD. The proposed method is set to feature in a forthcoming product from Coreline Soft.

Contact

Please leave your inquiry if you have any questions regarding our products, recruitment, investment, or any other matters.

Contact us