Artificial intelligence for imaging-based detection of gallbladder cancer: A systematic review and diagnostic test accuracy meta-analysis

doi:10.3748/wjg.123989

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 33, Issue 1

This Article

(2)

(0)

(86)

Peer-Review Report of This Article

Supplementary Materials of This Article

Journal Information of This Article

Publication Name

World Journal of Gastroenterology

ISSN

1007-9327

Publisher of This Article

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

Meta-Analysis

Copyright: ©Author(s) 2027. 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. Jan 7, 2027; 33(1): 123989
Published online Jan 7, 2027. doi: 10.3748/wjg.123989

Full Size Figure Download Figure

Figure 4 Robustness analyses. Pooled sensitivity and specificity are shown after leave-one-out exclusions and selected dataset restrictions. Numbers indicate contributing datasets; vertical orange lines mark the primary pooled estimates.

Citation: Zhu JY, Lin H, Zhao JW, Yang ZY. Artificial intelligence for imaging-based detection of gallbladder cancer: A systematic review and diagnostic test accuracy meta-analysis. World J Gastroenterol 2027; 33(1): 123989
URL: https://www.wjgnet.com/1007-9327/full/v33/i1/123989.htm
DOI: https://dx.doi.org/10.3748/wjg.123989

Zhu JY, Lin H, Zhao JW, Yang ZY. Artificial intelligence for imaging-based detection of gallbladder cancer: A systematic review and diagnostic test accuracy meta-analysis. World J Gastroenterol 2027; 33(1): 123989 [DOI: 10.3748/wjg.123989]

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.