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

Machine Learning (2004) Decision Support for Health Professionals (1492) Secondary Use of Clinical Data for Research and Surveillance (460) Gastroenterology and Hepatology (108)

Published on in Vol 10, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36997, first published .
Noninvasive Diagnosis of Nonalcoholic Steatohepatitis and Advanced Liver Fibrosis Using Machine Learning Methods: Comparative Study With Existing Quantitative Risk Scores

Noninvasive Diagnosis of Nonalcoholic Steatohepatitis and Advanced Liver Fibrosis Using Machine Learning Methods: Comparative Study With Existing Quantitative Risk Scores

Noninvasive Diagnosis of Nonalcoholic Steatohepatitis and Advanced Liver Fibrosis Using Machine Learning Methods: Comparative Study With Existing Quantitative Risk Scores

Authors of this article:

Yonghui Wu1 Author Orcid Image ;   Xi Yang1 Author Orcid Image ;   Heather L Morris2 Author Orcid Image ;   Matthew J Gurka1 Author Orcid Image ;   Elizabeth A Shenkman1 Author Orcid Image ;   Kenneth Cusi3 Author Orcid Image ;   Fernando Bril4 Author Orcid Image ;   William T Donahoo3 Author Orcid Image
Article Authors Cited by (12) Tweetations (4) Metrics

Journals

  1. Hassoun S, Bruckmann C, Ciardullo S, Perseghin G, Di Gaudio F, Broccolo F. Setting up of a machine learning algorithm for the identification of severe liver fibrosis profile in the general US population cohort. International Journal of Medical Informatics 2023;170:104932 View
  2. Wang K, Zhang L, Li L, Wang Y, Zhong X, Hou C, Zhang Y, Sun C, Zhou Q, Wang X. Identification of Drug-Induced Liver Injury Biomarkers from Multiple Microarrays Based on Machine Learning and Bioinformatics Analysis. International Journal of Molecular Sciences 2022;23(19):11945 View
  3. Zhang N, Guo F, Song Y. HOXC8/TGF-β1 positive feedback loop promotes liver fibrosis and hepatic stellate cell activation via activating Smad2/Smad3 signaling. Biochemical and Biophysical Research Communications 2023;662:39 View
  4. Hassoun S, Bruckmann C, Ciardullo S, Perseghin G, Marra F, Curto A, Arena U, Broccolo F, Di Gaudio F. NAIF: A novel artificial intelligence-based tool for accurate diagnosis of stage F3/F4 liver fibrosis in the general adult population, validated with three external datasets. International Journal of Medical Informatics 2024;185:105373 View
  5. Chen J, Wu Z, Liu H, Yan S, Wang Y, Xing M, Song X, Ding S. Cumulative effects of excess high-normal alanine aminotransferase levels in relation to new-onset metabolic dysfunction-associated fatty liver disease in China. World Journal of Gastroenterology 2024;30(10):1346 View
  6. Njei B, Osta E, Njei N, Al-Ajlouni Y, Lim J. An explainable machine learning model for prediction of high-risk nonalcoholic steatohepatitis. Scientific Reports 2024;14(1) View
  7. Velikova T, Gulinac M. Novel insights into autophagy in gastrointestinal pathologies, mechanisms in metabolic dysfunction-associated fatty liver disease and acute liver failure. World Journal of Gastroenterology 2024;30(27):3273 View
  8. Gao Y, Chen M, Li W, Liu Q, Jiao Y. Advancing the diagnosis and treatment of metabolic-associated steatotic liver disease: The transformative role of artificial intelligence. Artificial Intelligence in Gastroenterology 2025;6(1) View
  9. Beyzaei Z, Bagheri Z, Karimzadeh S, Geramizadeh B. Comparison of Histological Analysis and Gross Assessment for Detection of Donor Liver Macrovesicular Steatosis in Liver Transplantation: A Systematic Review and Meta-Analysis. Transplantation Proceedings 2025;57(6):1076 View
  10. Zhang H, Wu X, Ni W, Wu J, Zhou S, Jia L, Jin M, Zhao S, Jiang Z, Wu C, Sun Y, Shi J, Li J. Application of machine learning and deep learning in metabolic dysfunction-associated steatotic liver disease: a systematic review and meta-analysis. Journal of Advanced Research 2025 View
  11. Elmahdy M, Ali R, Khanna T, Gnanapandithan K, Kashani D. Artificial Intelligence in Cirrhosis: Diagnosis, Screening, and Evaluation for Liver Transplantation. F1000Research 2025;14:844 View

Conference Proceedings

  1. R G, Rani D, Anbarasu K. 2024 5th International Conference on Circuits, Control, Communication and Computing (I4C). Applying Deep Learning Methods to Non-Alcoholic Fatty Liver Disease Management View