| For: | Xiang Y, Yang N, Zheng TL, Huang YF, Liu TY, Ma DQ, Hu SJ, Zhang WH, Xiang HL, Zhang LY, Yuan LL, Wang X, Dang T, Zhang G, Wu B, Peng LJ, Gao M, Xia DL, Liu ZB, Li J, Song Y, Zhou XQ, Qi XS, Zeng J, Tan XY, Deng MM, Fang HM, Qi SL, He S, He YF, Ye B, Wu W, Shao JB, Wei W, Hu JP, Yong X, He CH, Bao JL, Zhang YN, Ji R, Bo Y, Yan W, Li HJ, Li SL, Geng S, Zhao L, Liu B, Qi XL. Development of a deep learning model for guiding treatment decisions of acute variceal bleeding in patients with cirrhosis. World J Gastroenterol 2025; 31(41): 111361 [PMID: 41257275 DOI: 10.3748/wjg.v31.i41.111361] |
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| URL: | https://www.wjgnet.com/1007-9327/full/v31/i41/111361.htm |
| Number | Citing Articles |
| 1 |
Ahemala Duishanbai, Yan-Bo Yu. Application of artificial intelligence model in precise risk stratification and treatment decision-making for acute variceal bleeding in cirrhosis. World Journal of Gastroenterology 2026; 32(30): 115546 doi: 10.3748/wjg.115546
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| 2 |
Mario Romeo, Fiammetta Di Nardo, Claudio Basile, Carmine Napolitano, Paolo Vaia, Luigi Di Puorto, Mattia Indipendente, Alessia De Gregorio, Marcello Dallio, Alessandro Federico. From FIB-4 to the Artificial Intelligence Era: The Evolution of Non-Invasive Tools (NITs) for Tailored Risk Stratification in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD). Livers 2026; 6(4) doi: 10.3390/livers6040071
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