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

Machine Learning (1975) Imaging Informatics (283) Artificial Intelligence (1999) Decision Support for Health Professionals (1471) Computer-Aided Diagnosis (226) Methods and Instruments in Medical Informatics (180) Tools, Programs and Algorithms (359) Clinical Informatics (1259)

Published on in Vol 9, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30066, first published .
Deep Learning Techniques for Fatty Liver Using Multi-View Ultrasound Images Scanned by Different Scanners: Development and Validation Study

Deep Learning Techniques for Fatty Liver Using Multi-View Ultrasound Images Scanned by Different Scanners: Development and Validation Study

Deep Learning Techniques for Fatty Liver Using Multi-View Ultrasound Images Scanned by Different Scanners: Development and Validation Study

Authors of this article:

Taewoo Kim1 Author Orcid Image ;   Dong Hyun Lee2 Author Orcid Image ;   Eun-Kee Park3 Author Orcid Image ;   Sanghun Choi1 Author Orcid Image
Article Authors Cited by (17) Tweetations (2) Metrics

Journals

  1. Micucci M, Iula A. Recent Advances in Machine Learning Applied to Ultrasound Imaging. Electronics 2022;11(11):1800 View
  2. Kim T, Kim W, Lee C, Chae K, Bak S, Kwon S, Jin G, Park E, Choi S. Quantitative computed tomography imaging-based classification of cement dust-exposed subjects with an artificial neural network technique. Computers in Biology and Medicine 2022;141:105162 View
  3. Yang Y, Liu J, Sun C, Shi Y, Hsing J, Kamya A, Keller C, Antil N, Rubin D, Wang H, Ying H, Zhao X, Wu Y, Nguyen M, Lu Y, Yang F, Huang P, Hsing A, Wu J, Zhu S. Nonalcoholic fatty liver disease (NAFLD) detection and deep learning in a Chinese community-based population. European Radiology 2023;33(8):5894 View
  4. Harrison A, Li B, Hsu T, Chen C, Yu W, Tai J, Lu L, Tai D. Steatosis Quantification on Ultrasound Images by a Deep Learning Algorithm on Patients Undergoing Weight Changes. Diagnostics 2023;13(20):3225 View
  5. Singh S, Hoque S, Zekry A, Sowmya A. Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review. Journal of Medical Systems 2023;47(1) View
  6. Zamanian H, Shalbaf A, Zali M, Khalaj A, Dehghan P, Tabesh M, Hatami B, Alizadehsani R, Tan R, Acharya U. Application of artificial intelligence techniques for non-alcoholic fatty liver disease diagnosis: A systematic review (2005–2023). Computer Methods and Programs in Biomedicine 2024;244:107932 View
  7. Fu Y, Zang D, Lin B, He Q, Xie Y, Zhang B, Liu Y, Jin Y, He Y, Guan T. A Deep Learning Method Enables Quantitative and Automatic Measurement of Rat Liver Histology in NAFLD. International Journal of Imaging Systems and Technology 2024;34(4) View
  8. Kaffas A, Bhatraju K, Vo-Phamhi J, Tiyarattanachai T, Antil N, Negrete L, Kamaya A, Shen L. Development of a Deep Learning Model for Classification of Hepatic Steatosis from Clinical Standard Ultrasound. Ultrasound in Medicine & Biology 2025;51(2):242 View
  9. Dai W, Wu Y, Ling Y, Zhao J, Zhang S, Gu Z, Gong L, Zhu M, Dong S, Xu S, Wu L, Sun L, Kong D. Development and validation of a deep learning pipeline to diagnose ovarian masses using ultrasound screening: a retrospective multicenter study. eClinicalMedicine 2024;78:102923 View
  10. Yin C, Zhang H, Du J, Zhu Y, Zhu H, Yue H. Artificial intelligence in imaging for liver disease diagnosis. Frontiers in Medicine 2025;12 View
  11. Yousefzamani M, Babapour Mofrad F. Deep learning without borders: recent advances in ultrasound image classification for liver diseases diagnosis. Expert Review of Medical Devices 2025;22(8):827 View
  12. Sui X, Lai K, Choy R, Wang H, Chan K, Aljufairi F, Zheng Y, Yip W, Young A, Tham C, Pang C, Li H, Chong K. Explainable Deep Learning System for Automatic Detection of Thyroid Eye Disease Using Facial Images. American Journal of Ophthalmology 2025;277:323 View
  13. Jagadeesh A, Aramrat C, Rai S, Maqsood F, Madhukeshwar A, Bhogadi S, Lieber J, Mahajan H, Banjara S, Lewin A, Kinra S, Mallinson P. Diagnostic accuracy of convolutional neural networks in classifying hepatic steatosis from B-mode ultrasound images: a systematic review with meta-analysis and novel validation in a community setting in Telangana, India. The Lancet Regional Health - Southeast Asia 2025;40:100644 View

Conference Proceedings

  1. Deng J, Li D, Zhang H, Yu X. 2023 International Conference on Computer Applications Technology (CCAT). A Study of Fatty Liver Prediction Model Based on XGBoost and SHAP View
  2. Pimpalkar A, Thorat N, Gulame M, Lokare D, Kulal N, Khune P. 2024 International Conference on Emerging Smart Computing and Informatics (ESCI). Partitions of Liver Cancer by Enhanced Feature Extraction and Mapping with Improved Transfer Developing Methods View
  3. Hailat H, Mustafa A, Najadat H, Alomari M, Freihat S. 2024 15th International Conference on Information and Communication Systems (ICICS). Addressing the Imbalanced Class Distribution in Fatty Liver Detection in CT Images Using Transfer Learning View
  4. Xu J, Wu X, Xu Z. 2024 7th International Conference on Information Communication and Signal Processing (ICICSP). GastroNet FusionAI: Artificial Intelligence Diagnosis of Clinical Gastric Cancer Tumors View