Role of Artificial Intelligence in Hepatitis B Virus-Associated Liver Injury Diagnosis Relationship
Keywords:
Artificial Intelligence (AI), Hepatitis B virus (HBV), Liver, Fibrosis, cirrhosis, hepatocellular carcinoma (HCC)Abstract
ABSTRACT
HBV infection is one of the most significant health problems worldwide and the most frequent causes of chronic liver disease, cirrhosis and hepatocellular carcinoma. The mechanism of liver injury in HBV infection is a complicated process that is catalyzed by the viral replication, host immune response and progressive fibrotic alteration. Knowledge of the HBV life cycle since the entry of the hepatocytes and covalently closed circulation DNA to viral replication and maintenance is crucial to correlate molecular pathogenesis of HBV with clinical liver damage. The traditional methods of diagnosis, such as serological markers, biochemical parameters and image procedures such as ultrasonography, computed tomography are important in evaluation of the disease. Artificial intelligence (AI) has recently become the hepatological power tool that helps to improve the accuracy of non-invasive imaging based and assessing liver fibrosis, inflammation and early hepatocellular carcinoma. Machine learning and deep learning algorithm make it possible to interpret images automatically, predict risk and disease progression. This review focuses on the interrelation between HBV viral pathogenesis and liver injury pathogenesis and the rising presence of AI-based diagnostic, monitoring and clinical decision-making in HBV related liver disease. ..
Downloads
References
[1.] T. Y. Li, Y. Yang, G. Zhou, and Z. K. Tu, “Immune suppression in chronic hepatitis B infection associated liver disease: A review,” World J. Gastroenterol., vol. 25, no. 27, p. 3527, Jul. 2019, doi: 10.3748/WJG.V25.I27.3527.
[2] J. Hu, “Hepatitis B Virus Virology and Replication,” pp. 1–34, 2016, doi: 10.1007/978-3-319-22330-8_1.
[3] L. S. Y. Tang, E. Covert, E. Wilson, and S. Kottilil, “Chronic Hepatitis B Infection: A Review,” JAMA, vol. 319, no. 17, pp. 1802–1813, May 2018, doi: 10.1001/JAMA.2018.3795.
[4] J. Mateo, A. María, T. Aranda, and N. Nishida, “Advancements in Artificial Intelligence-Enhanced Imaging Diagnostics for the Management of Liver Disease—Applications and Challenges in Personalized Care,” Bioeng. 2024, Vol. 11, Page 1243, vol. 11, no. 12, p. 1243, Dec. 2024, doi: 10.3390/BIOENGINEERING11121243.
[5] P. Bonnard et al., “Comparison of Elastography, Serum Marker Scores, and Histology for the Assessment of Liver Fibrosis in Hepatitis B Virus (HBV)-Infected Patients in Burkina Faso,” Am. J. Trop. Med. Hyg., vol. 82, no. 3, p. 454, Mar. 2010, doi: 10.4269/AJTMH.2010.09-0088.
[6] T. Bal, V. Hepatitin, T. Ve, Y. Yeni, B. Araç, and Y. Zeka, “A New Tool for the Diagnosis and Management of Viral Hepatitis: Artificial Intelligence,” Viral Hepat. J., vol. 30, no. 1, pp. 1–6, Apr. 2024, doi: 10.4274/VHD.GALENOS.2024.2023-12-4.
[7] X. Lu and T. Block, “Study of the early steps of the Hepatitis B Virus life cycle,” Int. J. Med. Sci., vol. 1, no. 1, p. 21, Jan. 2004, doi: 10.7150/IJMS.1.21.
[8] C. Li, C. Wei, and X. Yang, “Hepatitis B virus: modes of transmission, immune pathogenesis, and research progress on therapeutic vaccines,” Open Explor. 2019 36, vol. 3, no. 6, pp. 443–458, Oct. 2024, doi: 10.37349/EDD.2024.00060.
[9] Y. Wu, J. Wen, W. Xiao, and B. Zhang, “Pregenomic RNA: How to assist the management of chronic hepatitis B?,” Rev. Med. Virol., vol. 29, no. 4, p. e2051, Jul. 2019, doi: 10.1002/RMV.2051.
[10] T. J. Liang et al., “Present and future therapies of hepatitis B: From discovery to cure,” Hepatology, vol. 62, no. 6, pp. 1893–1908, Dec. 2015, doi: 10.1002/HEP.28025;ISSUE:ISSUE:DOI.
[11] L. Wei and A. Ploss, “Mechanism of Hepatitis B Virus cccDNA Formation,” Viruses 2021, Vol. 13, Page 1463, vol. 13, no. 8, p. 1463, Jul. 2021, doi: 10.3390/V13081463.
[12] L. Yuan et al., “Risk factors for progression to acute-on-chronic liver failure during severe acute exacerbation of chronic hepatitis B virus infection,” World J. Gastroenterol., vol. 25, no. 19, p. 2327, 2019, doi: 10.3748/WJG.V25.I19.2327.
[13] A. Adugna, “Antigen Recognition and Immune Response to Acute and Chronic Hepatitis B Virus Infection,” J. Inflamm. Res., vol. 16, pp. 2159–2166, 2023, doi: 10.2147/JIR.S411492.
[14] W. C. Zhou, Q. B. Zhang, and L. Qiao, “Pathogenesis of liver cirrhosis,” World J. Gastroenterol., vol. 20, no. 23, p. 7312, Jun. 2014, doi: 10.3748/WJG.V20.I23.7312.
[15] S. Y. Kim, Y. Y. Kyaw, and J. Cheong, “Functional interaction of endoplasmic reticulum stress and hepatitis B virus in the pathogenesis of liver diseases,” World J. Gastroenterol., vol. 23, no. 43, p. 7657, Nov. 2017, doi: 10.3748/WJG.V23.I43.7657.
[16] S. A. Alqahtani and M. Colombo, “Viral hepatitis as a risk factor for the development of hepatocellular carcinoma,” Hepatoma Res 2020;658., vol. 6, no. 0, p. N/A-N/A, Sep. 2020, doi: 10.20517/2394-5079.2020.49.
[17] G. Ali, M. M. Mijwil, I. Adamopoulos, B. A. Buruga, M. Gök, and M. Sallam, “Harnessing the Potential of Artificial Intelligence in Managing Viral Hepatitis,” Mesopotamian J. Big Data, vol. 2024, pp. 128–163, Aug. 2024, doi: 10.58496/MJBD/2024/010.
[18] S. A. Azer, “Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review,” World J. Gastrointest. Oncol., vol. 11, no. 12, p. 1218, Dec. 2019, doi: 10.4251/WJGO.V11.I12.1218.
[19] S. L. Popa et al., “Diagnosis of Liver Fibrosis Using Artificial Intelligence: A Systematic Review,” Med. 2023, Vol. 59, Page 992, vol. 59, no. 5, p. 992, May 2023, doi: 10.3390/MEDICINA59050992.
[20] J. H. Kao, “Diagnosis of hepatitis B virus infection through serological and virological markers,” Expert Rev. Gastroenterol. Hepatol., vol. 2, no. 4, pp. 553–562, Jun. 2008, doi: 10.1586/17474124.2.4.553;WGROUP:STRING:PUBLICATION.
[21] D. W. Zeng et al., “Serum HBsAg and HBeAg levels are associated with liver pathological stages in the immune clearance phase of hepatitis B virus chronic infection,” Mol. Med. Rep., vol. 11, no. 5, pp. 3465–3472, May 2015, doi: 10.3892/MMR.2015.3207/ABSTRACT.
[22] S. Liu, B. Zhou, J. D. Valdes, J. Sun, and H. Guo, “Serum Hepatitis B Virus RNA: A New Potential Biomarker for Chronic Hepatitis B Virus Infection,” Hepatology, vol. 69, no. 4, pp. 1816–1827, Apr. 2019, doi: 10.1002/HEP.30325;REQUESTEDJOURNAL:JOURNAL:15273350.
[23] J. H. Choi, “Histological and Molecular Evaluation of Liver Biopsies: A Practical and Updated Review,” Int. J. Mol. Sci. 2025, Vol. 26, Page 7729, vol. 26, no. 16, p. 7729, Aug. 2025, doi: 10.3390/IJMS26167729.
[24] C. Bera, N. Hamdan-Perez, and K. Patel, “Non-Invasive Assessment of Liver Fibrosis in Hepatitis B Patients,” J. Clin. Med. 2024, Vol. 13, Page 1046, vol. 13, no. 4, p. 1046, Feb. 2024, doi: 10.3390/JCM13041046.
[25] Y.-T. Shen, H.-X. Xu, and B.-Y. Zhou, “Advancements in Artificial Intelligence Applications for Liver Ultrasound Imaging,” BJR|Artificial Intell., Dec. 2025, doi: 10.1093/BJRAI/UBAF019.
[26] F. Alshomrani, “Recent Advances in Magnetic Resonance Imaging for the Diagnosis of Liver Cancer: A Comprehensive Review,” Diagnostics 2025, Vol. 15, Page 2016, vol. 15, no. 16, p. 2016, Aug. 2025, doi: 10.3390/DIAGNOSTICS15162016.
[27] M. M. Hussain, B. Feng, J. M. Wang, A. Q. Zhai, F. yu Li, and H. jie Hu, “Recent Advancements in Known and Emerging Risk Factors of Hepatocellular Carcinoma,” Cancer Med., vol. 14, no. 21, p. e71330, Nov. 2025, doi: 10.1002/CAM4.71330;PAGE:STRING:ARTICLE/CHAPTER.
[28] Y. Yu, M. Li, L. Liu, Y. Li, and J. Wang, “Clinical big data and deep learning: Applications, challenges, and future outlooks,” Big Data Min. Anal., vol. 2, no. 4, pp. 288–305, Dec. 2019, doi: 10.26599/BDMA.2019.9020007.
[29] G. Xiao, S. Zhu, X. Xiao, L. Yan, J. Yang, and G. Wu, “Comparison of laboratory tests, ultrasound, or magnetic resonance elastography to detect fibrosis in patients with nonalcoholic fatty liver disease: A meta-analysis,” Hepatology, vol. 66, no. 5, pp. 1486–1501, Nov. 2017, doi: 10.1002/HEP.29302;WGROUP:STRING:PUBLICATION..
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.