Machine learning model integrating transient elastography and clinical data for prediction of graft fibrosis after liver transplantation

doi:10.4254/wjh.120258

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 18, Issue 6

This Article

(26)

(33)

(0)

(304)

Peer-Review Report of This Article

CrossCheck and Google Search of This Article

Academic Rules and Norms of This Article

Supplementary Materials of This Article

Citation of this article

Corresponding Author of This Article

Research Domain of This Article

Article-Type of This Article

Open-Access Policy of This Article

Times Cited Counts in Google of This Article

Journal Information of This Article

Publication Name

World Journal of Hepatology

ISSN

1948-5182

Publisher of This Article

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

Retrospective Cohort Study

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 Hepatol. Jun 27, 2026; 18(6): 120258
Published online Jun 27, 2026. doi: 10.4254/wjh.120258

Full Size Figure Download Figure

Figure 4 Rule-of-thumb scatter plots. Scatter plots showing liver stiffness measurements (kPa) in relation to key predictive variables: A: Graft age; B: Aspartate aminotransferase; C: Patient age; D: Body mass index; E: Alkaline phosphatase; F: Hemoglobin; G: Donor type. Each point represents an individual patient. AST: Aspartate aminotransferase; BMI: Body mass index; ALP: Alkaline phosphatase; HGB: Hemoglobin.

Citation: Koivu A, Azarfar G, Shojaee M, Hlaing NKT, Rizvi S, Sharma D, Maleki S, Bhat M. Machine learning model integrating transient elastography and clinical data for prediction of graft fibrosis after liver transplantation. World J Hepatol 2026; 18(6): 120258
URL: https://www.wjgnet.com/1948-5182/full/v18/i6/120258.htm
DOI: https://dx.doi.org/10.4254/wjh.120258

Koivu A, Azarfar G, Shojaee M, Hlaing NKT, Rizvi S, Sharma D, Maleki S, Bhat M. Machine learning model integrating transient elastography and clinical data for prediction of graft fibrosis after liver transplantation. World J Hepatol 2026; 18(6): 120258 [DOI: 10.4254/wjh.120258]

All content on this site: Copyright © 1993-2026 Baishideng Publishing Group Inc, its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply.