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Articles Published Processes
10/15/2026 5:45:56 AM | Browse: 1 | Download: 2
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Received |
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2026-03-19 01:23 |
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Peer-Review Started |
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2026-03-19 01:24 |
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First Decision by Editorial Office Director |
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2026-04-13 09:34 |
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Return for Revision |
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2026-04-13 09:34 |
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Revised |
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2026-05-18 07:48 |
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Publication Fee Transferred |
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2026-05-29 11:45 |
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Second Decision by Editor |
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2026-07-03 02:40 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-07-03 06:42 |
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Articles in Press |
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2026-07-03 06:42 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-10-08 00:17 |
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Publish the Manuscript Online |
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2026-10-15 05:45 |
| ISSN |
1007-9327 (print) and 2219-2840 (online) |
| Open Access |
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. |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Retrospective Study |
| Article Title |
Domain specific training with three-dimensional virtual colonoscopy images improves artificial intelligence based automated size prediction for large lesions
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Dominik Schulz, Alanna Ebigbo, Eva Holley, Sandra Nagl, Olga Nichiporyk, Andreas Probst, Markus W Scheppach, Christoph Römmele, David Roser, Stephan Zellmer and Helmut Messmann |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Dominik Schulz, Senior Scientist, III. Medizinische Klinik, Universitätsklinikum Augsburg, Stenglinstraße 2, Augsburg 86156, Germany. dominik.schulz@uk-augsburg.de |
| Key Words |
Polyp size; Lesion size; Colonoscopy; Artificial intelligence; Monocular depth estimation |
| Core Tip |
Accurate measurement of colorectal polyp size is mandatory influencing the choice of resection technique and surveillance intervals. However, conventional visual estimation by endoscopists is prone to significant error rates up to 66%. In this work, we compare three state-of-the art monocular depth estimation artificial intelligence models for automated size measurement. When applied natively to colonoscopy, they show poor accuracy in size prediction. By domain specific training using virtual colonoscopy images we could significantly improve size prediction. The fine-tuned model achieved a mean absolute percentage error of 33.9%-37.0%. This performance was comparable to trainee endoscopists but remained inferior to experts. |
| Publish Date |
2026-10-15 05:45 |
| Citation |
Schulz D, Ebigbo A, Holley E, Nagl S, Nichiporyk O, Probst A, Scheppach MW, Römmele C, Roser D, Zellmer S, Messmann H. Domain specific training with three-dimensional virtual colonoscopy images improves artificial intelligence based automated size prediction for large lesions. World J Gastroenterol 2026; 32(45): 121242
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| URL |
https://www.wjgnet.com/1007-9327/full/v32/i45/121242.htm |
| DOI |
https://doi.org/10.3748/wjg.121242 |
Copyright © 1993-2026 Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA. All rights reserved, including rights relating to text and data mining, AI training, and similar technologies. For open-access content, the applicable copyright and licensing terms govern.
