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

Clinical Information and Decision Making (1915) Tools, Programs and Algorithms (376) Artificial Intelligence (2129) Machine Learning (2076) Imaging Informatics (302) Decision Support for Health Professionals (1539) New Methods (1134)

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
Article Authors Cited by (14) Tweetations (4) Metrics

Journals

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  5. Hou B, Lee S, Lee J, Koh C, Xiao J, Pickhardt P, Summers R. Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification. Radiology: Artificial Intelligence 2024;6(5) View
  6. Aisen A, Rodrigues P. Fluid Intelligence: AI’s Role in Accurate Measurement of Ascites. Radiology: Artificial Intelligence 2024;6(5) View
  7. Song B, Jiang H, Liu J, Yu Y, Luan J, Zhao Y, Wang Y, Zhang J, Liu Z, Zhang N, Zhu X, Ma Z. Deep Learning-Assisted Real-Time Wall Shear Stress Measurement on Chicken Embryo Heart Using Spectral Domain Optical Coherence Tomography. IEEE Transactions on Instrumentation and Measurement 2024;73:1 View
  8. Lin Z, Zheng J, Deng Y, Du L, Liu F, Li Z. Deep learning-aided diagnosis of acute abdominal aortic dissection by ultrasound images. Emergency Radiology 2025;32(2):233 View
  9. Yang L, Zhang X, Li Z, Wang J, Zhang Y, Shan L, Shi X, Si Y, Wang S, Li L, Wu P, Xu N, Liu L, Yang J, Leng J, Yang M, Zhang Z, Wang J, Dong X, Yang G, Yan R, Li W, Liu Z, Li W. Localization and Classification of Adrenal Masses in Multiphase Computed Tomography: Retrospective Study. Journal of Medical Internet Research 2025;27:e65937 View
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Books/Policy Documents

  1. Przybyszewski E, Simon T, Chung R. Artificial Intelligence, Machine Learning, and Deep Learning in Precision Medicine in Liver Diseases. View

Conference Proceedings

  1. Nag M, Liu J, Liu L, Shin S, lee S, Lee J, Summers R, Linguraru M, Rittner L, Lepore N, Romero Castro E, Brieva J, Guevara P. 18th International Symposium on Medical Information Processing and Analysis. Body location embedded 3D U-Net (BLE-U-Net) for ovarian cancer ascites segmentation on CT scans View
  2. Nag M, Liu J, Shin S, Hou B, Liu L, Pickhardt P, Lee J, Summers R, Iftekharuddin K, Chen W. Medical Imaging 2023: Computer-Aided Diagnosis. Improved ascites segmentation with bladder identification using anatomical location residual U-Net View