Artificial Intelligence for Dynamic Echocardiographic Tricuspid Valve Analysis : A New Tool in Echocardiography

Copyright © 2020 Elsevier Inc. All rights reserved..

There has been a resurgence of interest in the structure and function of the tricuspid valve (TV) with the established prognostic impact of functional tricuspid regurgitation. Current 3-dimensional transesophageal echocardiography prototype software is limited to exploration of the mitral and aortic valves exclusively. Thus, newer analytical software is required for dynamic geometric analysis of the TV morphology for remodeling. This article presents a preliminary experience with novel artificial intelligence-based semiautomated software for TV analysis. The software offers high correlation to surgical inspection by its ability to analyze morphology and dynamics of the valve throughout the cardiac cycle. In addition, it allows higher reproducibility of data analysis and reduces interobserver variability with minimal need for manual intervention. Integration of interactivity through preprocedural placement of specific devices of different sizes and shapes in the mitral and aortic positions facilitates prognostic evaluation of surgical and interventional procedures.

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:34

Enthalten in:

Journal of cardiothoracic and vascular anesthesia - 34(2020), 10 vom: 01. Okt., Seite 2703-2706

Sprache:

Englisch

Beteiligte Personen:

Fatima, Huma [VerfasserIn]
Mahmood, Feroze [VerfasserIn]
Sehgal, Sankalp [VerfasserIn]
Belani, Kiran [VerfasserIn]
Sharkey, Aidan [VerfasserIn]
Chaudhary, Omar [VerfasserIn]
Baribeau, Yanick [VerfasserIn]
Matyal, Robina [VerfasserIn]
Khabbaz, Kamal R [VerfasserIn]

Links:

Volltext

Themen:

Artificial intelligence
Journal Article
Review
Tricuspid valve
Tricuspid valve regurgitation

Anmerkungen:

Date Completed 27.04.2021

Date Revised 27.04.2021

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1053/j.jvca.2020.04.056

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM311193137