Automated diagnosis of heart valve degradation using novelty detection algorithms and machine learning.

ABSTRACT: The blood flow through the major vessels holds great diagnostic potential for the identification of cardiovascular complications and is therefore routinely assessed with current diagnostic modalities. Heart valves are subject to high hydrodynamic loads which render them prone to premature degradation. Failing native aortic valves are routinely replaced with bioprosthetic heart valves. This type of prosthesis is limited by a durability that is often less than the patient's life expectancy. Frequent assessment of valvular function can therefore help to ensure good long-term outcomes and to plan reinterventions. In this article, we describe how unsupervised novelty detection algorithms can be used to automate the interpretation of blood flow data to improve outcomes through early detection of

SUBMITTER: Vennemann B 

PROVIDER: S-EPMC6762068 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature