Domain specific training with three-dimensional virtual colonoscopy images improves artificial intelligence based automated size prediction for large lesions

doi:10.3748/wjg.121242

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World Journal of Gastroenterology

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1007-9327

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

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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 Gastroenterol. Dec 7, 2026; 32(45): 121242
Published online Dec 7, 2026. doi: 10.3748/wjg.121242

Full Size Figure Download Figure

Figure 1 Violin plot displaying ground truth lesion size distribution of the endoscopic submucosal dissection and the external dataset. Average lesion size was much smaller in the external dataset compared to the endoscopic submucosal dissection set. ESD: Endoscopic submucosal dissection.

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
URL: https://www.wjgnet.com/1007-9327/full/v32/i45/121242.htm
DOI: https://dx.doi.org/10.3748/wjg.121242

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 [DOI: 10.3748/wjg.121242]

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