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

PURPOSE: Thoracic 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 read...

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Detalles Bibliográficos
Autores principales: Pradella, Maurice, Achermann, Rita, Sperl, Jonathan I., Kärgel, Rainer, Rapaka, Saikiran, Cyriac, Joshy, Yang, Shan, Sommer, Gregor, Stieltjes, Bram, Bremerich, Jens, Brantner, Philipp, Sauter, Alexander W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Cardiovascular Medicine
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9441594/
https://www.ncbi.nlm.nih.gov/pubmed/36072871
http://dx.doi.org/10.3389/fcvm.2022.972512

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9441594/
https://www.ncbi.nlm.nih.gov/pubmed/36072871
http://dx.doi.org/10.3389/fcvm.2022.972512

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