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World J Gastroenterol. Nov 14, 2026; 32(42): 120151
Published online Nov 14, 2026. doi: 10.3748/wjg.120151
Published online Nov 14, 2026. doi: 10.3748/wjg.120151
Realizing personalized prediction for the reversal of chronic hepatitis B-associated liver fibrosis through machine learning
Shi-Cai Liu, Zi-Wen Wang, School of Medical Information, Wannan Medical University, Wuhu 241002, Anhui Province, China
Shi-Cai Liu, Anhui Province High-Quality Dataset Construction Base for Smart Healthcare, Wannan Medical University, Wuhu 241002, Anhui Province, China
Han Zhang, School of Basic Medical Sciences, Wannan Medical University, Wuhu 241002, Anhui Province, China
Author contributions: Liu SC conceived and outlined the manuscript, contributed to the discussion and the manuscript preparation; Liu SC, Wang ZW and Zhang H contributed to the writing and editing of the manuscript, and review of the literature; all authors have read and approved the final version of the manuscript.
Supported by the Scientific Research Project of Anhui Provincial Department of Education, No. 2025AHGXZK30665; and Talent Scientific Research Start-up Foundation of Wannan Medical College, No. WYRCQD2023045.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Corresponding author: Shi-Cai Liu, PhD, School of Medical Information, Wannan Medical University, No. 22 Wenchang West Road, Wuhu 241002, Anhui Province, China.
Received: February 24, 2026
Revised: March 30, 2026
Accepted: April 14, 2026
Published online: November 14, 2026
Processing time: 217 Days and 22.8 Hours
Revised: March 30, 2026
Accepted: April 14, 2026
Published online: November 14, 2026
Processing time: 217 Days and 22.8 Hours
Core Tip
Core Tip: Predicting liver fibrosis reversal in chronic hepatitis B remains a major clinical challenge due to the limitations of conventional assessment tools. Leveraging machine learning-driven histopathological image analysis and multimodal data fusion, enhances the accuracy and individualization of predicting treatment response during antiviral therapy. Integrating quantitative pathological features, clinical parameters, and proteomic biomarkers into unified machine learning frameworks optimizes early identification of non-responders, supports personalized therapeutic decision-making for chronic hepatitis B patients.