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. 2024 Apr 18;38(6):3212–3222. doi: 10.1007/s00464-024-10827-6

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© The Author(s) 2024

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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/4.0/.

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Fig. 4 — A composite image illustrating an augmented reality per pixel transection recommendation using Formula 1 for a sigmoid resection for diverticular disease on top (a, proximal on the right) and for T1N1 adenocarcinoma below (b, proximal on the left). The panel on the top left (a.1 and b.1) shows the white light view, the one in the middle left (a.2 and b.2) shows the raw NIR feed, and the ones on the bottom left (a.3 and b.3) show an overlay view (composed of the NIR feed overlaid on the white light feed). These three images are unchanged from the display of the camera system. The main large panels on the right (a.4 and b.5) show the computed recommendation augmented on the white light view. Computational recommendation (legend top right of a.4 and b.4) with green indicating sufficient, orange borderline and red insufficient perfusion (see supplementary material)