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

Machine Learning (1465) Decision Support for Health Professionals (1185) Secondary Use of Clinical Data for Research and Surveillance (367) Gastroenterology and Hepatology (68)

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 (7) 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