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. 2024 Sep 28;14:22508. doi: 10.1038/s41598-024-73434-4

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

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Fig. 4 — Comparison between the results inferred by the AI model and ICG cholangiography on the test set. The following five examples showcase the results obtained by the proposed AI model when applied to videos it had never seen during the training process. (a) Original image, (b) The AI model’s predictions for bile duct (BD) in green and anterior wall (AW) in blue. The white arrows point to the actual transection site as confirmed in (c,d) ICG cholangiography, (d) Image after bile duct transection, with white arrows indicating the site where the bile duct was transected.