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

Clinical Information and Decision Making (1407) Tools, Programs and Algorithms (266) Artificial Intelligence (1371) Machine Learning (1465) Imaging Informatics (193) Decision Support for Health Professionals (1186) New Methods (945)

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 (10) Tweetations (4) Metrics

Journals

  1. Теплякова А, Старков С. APPLICATION OF COMPUTER VISION FOR DIAGNOSTICS OF NOSOLOGICAL UNITS ON MEDICAL IMAGES. Южно-Сибирский научный вестник 2022;(4(44)):134 View
  2. Farajollahi M, Safarian M, Hatami M, Esmaeil Nejad A, Peters O. Applying artificial intelligence to detect and analyse oral and maxillofacial bone loss—A scoping review. Australian Endodontic Journal 2023;49(3):720 View
  3. Lee Y, Shin H, Kim J, Lee J. A Convolutional Neural Network for Classification of Stimuli Based on Stretchable Mechanical Sensor. IEEE Sensors Journal 2023;23(17):20338 View
  4. Sun J, Li H, Liu Z, Wang S, Peng Y. Impact of reconstruction algorithms on the success rate and quality of automatic airway segmentation in children under ultra-low-dose chest CT scanning. International Journal of Radiation Research 2024;22(1):171 View
  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

Books/Policy Documents

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