This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (1328) Tools, Programs and Algorithms (243) Artificial Intelligence (1198) Machine Learning (1385) Imaging Informatics (187) Decision Support for Health Professionals (1137) New Methods (909)

Published on in Vol 24, No 1 (2022): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34415, first published .
A Deep Residual U-Net Algorithm for Automatic Detection and Quantification of Ascites on Abdominopelvic Computed Tomography Images Acquired in the Emergency Department: Model Development and Validation

A Deep Residual U-Net Algorithm for Automatic Detection and Quantification of Ascites on Abdominopelvic Computed Tomography Images Acquired in the Emergency Department: Model Development and Validation

A Deep Residual U-Net Algorithm for Automatic Detection and Quantification of Ascites on Abdominopelvic Computed Tomography Images Acquired in the Emergency Department: Model Development and Validation

Authors of this article:

Hoon Ko1 Author Orcid Image ;   Jimi Huh2 Author Orcid Image ;   Kyung Won Kim3, 4 Author Orcid Image ;   Heewon Chung1 Author Orcid Image ;   Yousun Ko5 Author Orcid Image ;   Jai Keun Kim2 Author Orcid Image ;   Jei Hee Lee2 Author Orcid Image ;   Jinseok Lee1 Author Orcid Image
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