Integration of clinical parameters and CT-based radiomics improves machine learning assisted subtyping of primary hyperaldosteronism

Integration of clinical parameters and CT-based radiomics improves machine learning assisted subtyping of primary hyperaldosteronism

ObjectivesThe aim of this study was to investigate an integrated diagnostics approach for prediction of the source of aldosterone overproduction in primary hyperaldosteronism (PA).Methods269 patients from the prospective German Conn Registry with PA were included in this study. After segmentation of...

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Bibliographic Details
Main Authors: Nabeel Mansour, Andreas Mittermeier, Roman Walter, Balthasar Schachtner, Jan Rudolph, Bernd Erber, Vanessa F. Schmidt, Daniel Heinrich, Denise Bruedgam, Lea Tschaidse, Hanna Nowotny, Martin Bidlingmaier, Sonja L. Kunz, Christian Adolf, Jens Ricke, Martin Reincke, Nicole Reisch, Moritz Wildgruber, Michael Ingrisch
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-08-01
Series:Frontiers in Endocrinology
Subjects:
machine learning
hyperaldosteronism
adrenal venous sampling
integrated diagnostics
venous interventions
Online Access:https://www.frontiersin.org/articles/10.3389/fendo.2023.1244342/full

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https://www.frontiersin.org/articles/10.3389/fendo.2023.1244342/full

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