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.

Journal: Journal of medical Internet research
PMID: 34982041

Abstract

BACKGROUND: Detection and quantification of intra-abdominal free fluid (ie, ascites) on computed tomography (CT) images are essential processes for finding emergent or urgent conditions in patients. In an emergency department, automatic detection and quantification of ascites will be beneficial.

Authors

  • Hoon Ko
  • Jimi Huh
    Department of Radiology, Ajou University School of Medicine and Graduate School of Medicine, Ajou University Hospital, Suwon, Korea.
  • Kyung Won Kim
    Department of Pediatrics, Severance Children's Hospital, Institute of Allergy, Institute for Immunology and Immunological Diseases, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea. kwkim@yuhs.ac.
  • Heewon Chung
  • Yousun Ko
    Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea.
  • Jai Keun Kim
    The Department of Radiology, Ajou University School of Medicine, Suwon, Republic of Korea.
  • Jei Hee Lee
    The Department of Radiology, Ajou University School of Medicine, Suwon, Republic of Korea.
  • Jinseok Lee

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