Welcome to brand new Ask the Fish. Post your questions in different boards to get in touch with CTisus team & Dr. Elliot K. Fishman!
Our old Ask the Fish forum can be still viewed as an archive at https://ctisus.com/redesign-askfish/index.html.
We encourage all the users to register in this new forum to get answers to their questions since the posts in old forum will no longer be reviewed!
Thank you for visiting & looking forward to your feedback!

Author Topic: Using a Deep Learning Network to Diagnose Congestive Heart Failure.  (Read 4469 times)

Lilly Kauffman

  • Global Moderator
  • Newbie
  • *****
  • Posts: 34
    • View Profile
    • CTisus
Using a Deep Learning Network to Diagnose Congestive Heart Failure.

Long H. Ngo

Medical imaging technologies such as radiography, US, CT, and, increasingly, MRI are indispensable in the screening and diagnosis of diseases of the heart, lungs, bones, and other organs. Chest radiography to detect congestive heart failure (CHF) and related complications is one of the most common radiologic procedures performed in the United States. A positive screening result for CHF at chest radiography may be followed by serum B-type natriuretic peptide (BNP) assessment to help confirm the diagnosis. The diagnostic accuracy of BNP in the detection of CHF is excellent, with a sensitivity above 95%. However, BNP testing is not performed for all patients, the laboratory test is expensive, and the final result may be delayed if an off-site laboratory is used. In the absence of BNP data, what is the loss in diagnostic accuracy of using just the chest radiograph? Is there a data-driven solution to compensate for this loss?

DOI: https://doi.org/10.1148/radiol.2018182341


What are your thoughts?  Comments?