Performance of a deep learning tool to detect missed aortic dilatation in a large chest CT cohort

Performance of a deep learning tool to detect missed aortic dilatation in a large chest CT cohort

PurposeThoracic aortic (TA) dilatation (TAD) is a risk factor for acute aortic syndrome and must therefore be reported in every CT report. However, the complex anatomy of the thoracic aorta impedes TAD detection. We investigated the performance of a deep learning (DL) prototype as a secondary readin...

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Bibliographic Details
Main Authors: Maurice Pradella, Rita Achermann, Jonathan I. Sperl, Rainer Kärgel, Saikiran Rapaka, Joshy Cyriac, Shan Yang, Gregor Sommer, Bram Stieltjes, Jens Bremerich, Philipp Brantner, Alexander W. Sauter
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-08-01
Series:Frontiers in Cardiovascular Medicine
Subjects:
aorta - thoracic
aortic aneurysm (thoracic)
deep learning
dilatation
computed tomography
guidelines
Online Access:https://www.frontiersin.org/articles/10.3389/fcvm.2022.972512/full

Internet

https://www.frontiersin.org/articles/10.3389/fcvm.2022.972512/full

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