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Cited by in F6Publishing
For: Park SH, Park HM, Baek KR, Ahn HM, Lee IY, Son GM. Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery. World J Gastroenterol 2020; 26(44): 6945-6962 [PMID: 33311942 DOI: 10.3748/wjg.v26.i44.6945] [Cited by in CrossRef: 14] [Cited by in F6Publishing: 11] [Article Influence: 7.0] [Reference Citation Analysis]
Number Citing Articles
1 Arpaia P, Bracale U, Corcione F, De Benedetto E, Di Bernardo A, Di Capua V, Duraccio L, Peltrini R, Prevete R. Assessment of blood perfusion quality in laparoscopic colorectal surgery by means of Machine Learning. Sci Rep 2022;12:14682. [PMID: 36038561 DOI: 10.1038/s41598-022-16030-8] [Reference Citation Analysis]
2 Yotsov T, Karamanliev M, Maslyankov S, Iliev S, Ramadanov N, Dimitrov D. Mesenteric Vascular Evaluation with Pre-operative Multidetector Computed Tomographic Angiography and Intraoperative Indocyanine Green Angiography to Reduce Anastomotic Leaks after Minimally Invasive Surgery for Colorectal Cancer. JSLS 2022;26:e2022. [PMID: 35967960 DOI: 10.4293/JSLS.2022.00022] [Reference Citation Analysis]
3 Helmy Abdou MA, Truong TT, Dykky A, Ferreira P, Jul E. CapillaryNet: An automated system to quantify skin capillary density and red blood cell velocity from handheld vital microscopy. Artificial Intelligence in Medicine 2022;127:102287. [DOI: 10.1016/j.artmed.2022.102287] [Reference Citation Analysis]
4 Taha-Mehlitz S, Däster S, Bach L, Ochs V, von Flüe M, Steinemann D, Taha A. Modern Machine Learning Practices in Colorectal Surgery: A Scoping Review. J Clin Med 2022;11:2431. [PMID: 35566555 DOI: 10.3390/jcm11092431] [Reference Citation Analysis]
5 Daniluk P, Mazur N, Swierblewski M, Chand M, Diana M, Polom K. Fluorescence Imaging in Colorectal Surgery: An Updated Review and Future Trends. Surg Innov 2022;:15533506211072678. [PMID: 35232304 DOI: 10.1177/15533506211072678] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Polom W, Migaczewski M, Skokowski J, Swierblewski M, Cwalinski T, Kalinowski L, Pedziwiatr M, Matuszewski M, Polom K. Multispectral Imaging Using Fluorescent Properties of Indocyanine Green and Methylene Blue in Colorectal Surgery-Initial Experience. J Clin Med 2022;11:368. [PMID: 35054062 DOI: 10.3390/jcm11020368] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
7 Gomez-Rosado JC, Valdes-Hernandez J, Cintas-Catena J, Cano-Matias A, Perez-Sanchez A, Del Rio-Lafuente FJ, Torres-Arcos C, Lara-Fernandez Y, Capitan-Morales LC, Oliva-Mompean F. Feasibility of quantitative analysis of colonic perfusion using indocyanine green to prevent anastomotic leak in colorectal surgery. Surg Endosc 2022;36:1688-95. [PMID: 34988740 DOI: 10.1007/s00464-021-08918-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
8 Hardy NP, Cahill RA. Digital surgery for gastroenterological diseases. World J Gastroenterol 2021; 27(42): 7240-7246 [PMID: 34876786 DOI: 10.3748/wjg.v27.i42.7240] [Cited by in CrossRef: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Hardy NP, Dalli J, Mac Aonghusa P, Neary PM, Cahill RA. Biophysics inspired artificial intelligence for colorectal cancer characterization. Artif Intell Gastroenterol 2021; 2(3): 77-84 [DOI: 10.35712/aig.v2.i3.77] [Reference Citation Analysis]
10 Ahn HM, Son GM, Lee IY, Park SH, Kim NS, Baek KR. Optimization of indocyanine green angiography for colon perfusion during laparoscopic colorectal surgery. Colorectal Dis 2021;23:1848-59. [PMID: 33894016 DOI: 10.1111/codi.15684] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Pampiglione T, Chand M. Enhancing colorectal anastomotic safety with indocyanine green fluorescence angiography: An update. Surg Oncol 2021;:101545. [PMID: 33820705 DOI: 10.1016/j.suronc.2021.101545] [Reference Citation Analysis]