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

Table 2 Results from the two validation cohorts

Dataset	Mean correctly classified binned Sizes (%)
Dataset	AplDepth Pro	UniK3D	DphAnyV2	DphAnyV2 3DVCol	Expert	Trainee
Endoscopic submucosal dissection dataset	53.5	50.3	37.0	59.8	61.9	39.9
External dataset	42.9	52.4	54.8	59.5

Displayed is the overall correctly classified binned sizes, i.e. if the models or humans have correctly predicted the polyp size within the bins: (1) 0-5 mm; (2) 6-9 mm; (3) 10-19 mm; (4) 20-39 mm; and (5) > 39 mm.

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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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