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For: Wong GL, Yuen PC, Ma AJ, Chan AW, Leung HH, Wong VW. Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis. J Gastroenterol Hepatol 2021;36:543-50. [PMID: 33709607 DOI: 10.1111/jgh.15385] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 8.0] [Reference Citation Analysis]
Number Citing Articles
1 Wong GL, Hui VW, Tan Q, Xu J, Lee HW, Yip TC, Yang B, Tse Y, Yin C, Lyu F, Lai JC, Lui GC, Chan HL, Yuen P, Wong VW. Novel machine-learning models outperform risk scores in predicting hepatocellular carcinoma in patients with chronic viral hepatitis. JHEP Reports 2022. [DOI: 10.1016/j.jhepr.2022.100441] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Glass C, Lafata KJ, Jeck W, Horstmeyer R, Cooke C, Everitt J, Glass M, Dov D, Seidman MA. The Role of Machine Learning in Cardiovascular Pathology. Can J Cardiol 2021:S0828-282X(21)00867-9. [PMID: 34813876 DOI: 10.1016/j.cjca.2021.11.008] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
3 Li X, Kang N, Qi X, Huang Y. Artificial intelligence in the diagnosis of cirrhosis and portal hypertension. J Med Ultrason (2001) 2021. [PMID: 34787742 DOI: 10.1007/s10396-021-01153-8] [Reference Citation Analysis]
4 Zhuang H, Zhang J, Liao F. A systematic review on application of deep learning in digestive system image processing. Vis Comput 2021;:1-16. [PMID: 34744231 DOI: 10.1007/s00371-021-02322-z] [Reference Citation Analysis]
5 Dai G, Zhang X, Liu W, Li Z, Wang G, Liu Y, Xiao Q, Duan L, Li J, Song X, Li G, Bai S. Analysis of EPID Transmission Fluence Maps Using Machine Learning Models and CNN for Identifying Position Errors in the Treatment of GO Patients. Front Oncol 2021;11:721591. [PMID: 34595115 DOI: 10.3389/fonc.2021.721591] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
6 Alqahtani SA, Schattenberg JM. Nonalcoholic fatty liver disease: use of diagnostic biomarkers and modalities in clinical practice. Expert Rev Mol Diagn 2021;:1-14. [PMID: 34346799 DOI: 10.1080/14737159.2021.1964958] [Reference Citation Analysis]
7 Li Y, Wang X, Zhang J, Zhang S, Jiao J. Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review. Rev Endocr Metab Disord 2021. [PMID: 34396467 DOI: 10.1007/s11154-021-09681-x] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Finkelman BS, Meindl A, LaBoy C, Griffin B, Narayan S, Brancamp R, Siziopikou KP, Pincus JL, Blanco LZ Jr. Correlation of manual semi-quantitative and automated quantitative Ki-67 proliferative index with OncotypeDXTM recurrence score in invasive breast carcinoma. Breast Dis 2021. [PMID: 34397396 DOI: 10.3233/BD-201011] [Reference Citation Analysis]
9 Vaz K, Goodwin T, Kemp W, Roberts S, Majeed A. Artificial Intelligence in Hepatology: A Narrative Review. Semin Liver Dis 2021. [PMID: 34327698 DOI: 10.1055/s-0041-1731706] [Reference Citation Analysis]
10 García-Carretero R, Holgado-Cuadrado R, Barquero-Pérez Ó. Assessment of Classification Models and Relevant Features on Nonalcoholic Steatohepatitis Using Random Forest. Entropy (Basel) 2021;23:763. [PMID: 34204225 DOI: 10.3390/e23060763] [Reference Citation Analysis]
11 Yip TC, Wong VW, Wong GL. Liver-heart connection in diabetes mellitus. J Gastroenterol Hepatol 2021;36:1385-6. [PMID: 34105827 DOI: 10.1111/jgh.15533] [Reference Citation Analysis]
12 van der Laak J, Litjens G, Ciompi F. Deep learning in histopathology: the path to the clinic. Nat Med 2021;27:775-84. [PMID: 33990804 DOI: 10.1038/s41591-021-01343-4] [Cited by in Crossref: 4] [Cited by in F6Publishing: 65] [Article Influence: 4.0] [Reference Citation Analysis]