Deep learning-based multimodal model for predicting on-treatment histological outcomes in chronic hepatitis B-associated advanced liver fibrosis

doi:10.3748/wjg.v32.i15.116679

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Gastroenterol. Apr 21, 2026; 32(15): 116679
Published online Apr 21, 2026. doi: 10.3748/wjg.v32.i15.116679

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Figure 4 Subgroup analysis. A: Multimodal model performance in the progressive fibrosis (orange) and cirrhosis (blue) subgroup; B: Multimodal model performance in the hepatitis B e antigen-positive (orange) and hepatitis B e antigen-negative subgroup (blue). HBeAg: Hepatitis B e antigen; ROC: Receiver operating characteristic; AUC: Area under the receiver operating characteristic curve.

Citation: Han W, Cheng DY, He QW, Wang SH, Gong SJ, Chen Y, Yang YP. Deep learning-based multimodal model for predicting on-treatment histological outcomes in chronic hepatitis B-associated advanced liver fibrosis. World J Gastroenterol 2026; 32(15): 116679
URL: https://www.wjgnet.com/1007-9327/full/v32/i15/116679.htm
DOI: https://dx.doi.org/10.3748/wjg.v32.i15.116679

Han W, Cheng DY, He QW, Wang SH, Gong SJ, Chen Y, Yang YP. Deep learning-based multimodal model for predicting on-treatment histological outcomes in chronic hepatitis B-associated advanced liver fibrosis. World J Gastroenterol 2026; 32(15): 116679 [DOI: 10.3748/wjg.v32.i15.116679]

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