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]
Reader's ID:
03664074
Submitted on:
November 06, 2025, 08:49
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Reader Comments:
This study has compared several computational methods to be used in the predication of prognosis for acute variceal bleeding in cirrhosis patients. Author claimed that the convolution network by their efforts displayed the best effect in the outcomes analysis rather than another methods. However, the specific features in different subgroups with data presentations using AI or other computational methods lacked in the full analysis to be performed in the compared computation process. This means that this study only provides the end result for supporting their analysis as they wanted. In addition, the standard between high and low risk group presented to differ significantly without clinical parameters in this study.