Discrimination between arterial and venous bowel ischemia by computer-assisted analysis of the fluorescent signal
Arterial blood supply deficiency and venous congestion both play a role in anastomotic complications. Our aim was to evaluate a software-based analysis of the fluorescence signal to recognize the patterns of bowel ischemia.
In 18 pigs, two clips were applied on the inferior mesenteric artery (group A: n = 6) or vein (group V: n = 6) or on both (group A–V: n = 6). Three regions of interest (ROIs) were identified on the sigmoid: P = proximal to the first clip; C = central, between the two clips; and D = distal to the second clip. Indocyanine Green was injected intravenously. The fluorescence signal was captured by means of a near-infrared laparoscope. The time-to-peak (seconds) and the maximum fluorescence intensity were recorded using software. A normalized fluorescence intensity unit (NFIU: 0-to-1) was attributed, using a reference card. The NFIU’s over-time variations were computed every 10 min for 50 min. Capillary lactates were measured on the sigmoid at the 3 ROIs. Various machine learning algorithms were applied for ischemia patterns recognition.
The time-to-peak at the ischemic ROI C was significantly longer in group A versus V (20.1 ± 13 vs. 8.43 ± 3.7; p = 0.04) and in group A–V versus V (20.71 ± 11.6 vs. 8.43 ± 3.7; p = 0.03). The maximal NIFU at ROI C, was higher in the V group (1.01 ± 0.21) when compared to A (0.61 ± 0.11; p = 0.002) and A–V (0.41 ± 0.2; p = 0.0005). Capillary lactates at ROI C were lower in V (1.3 ± 0.6) than in A (1.9 ± 0.5; p = 0.0071), and A–V (2.6 ± 1.5; p = 0.034). The K nearest neighbor and the Linear SVM algorithms provided both an accuracy of 75% in discriminating between A versus V and 85% in discriminating A versus A–V. The accuracy dropped to 70% when the ML had to identify the ROI and the type of ischemia simultaneously.
The computer-assisted dynamic analysis of the fluorescence signal enables the discrimination between different bowel ischemia models.
KeywordsFluorescence angiography Fluorescence-based Enhanced Reality Computer-assisted analysis of fluorescence signal Tissue perfusion Machine learning
Authors are grateful to Christopher Burel, professional in Medical English proofreading for his assistance with the manuscript revision.
This study was funded by the ARC Foundation for Cancer Research, a French foundation entirely dedicated to cancer research, in the framework of a large project (ELIOS: Endoscopic Luminescent Imaging for precision Oncologic Surgery) aiming at the development of fluorescence-guided surgery. https://www.fondation-arc.org/projets/ameliorer-diagnostic-et-traitement-chirurgical-cancers-digestifs.
Compliance with ethical standards
Michele Diana is the recipient of the ELIOS grant from the ARC foundation. Jacques Marescaux is the President of both IRCAD and IHU-Strasbourg Institutes, which are partly funded by Karl Storz, Medtronic and Siemens Healthcare. Giuseppe Quero, Alfonso Lapergola, Manuel Barberio, Barbara Seeliger, Ines Gockel, Paola Saccomandi, Ludovica Guierriero, Didier Mutter, Alend Saadi, Marc Worreth and Vincent Agnus have no conflicts of interest or financial ties to disclose.
- 4.Ohi M, Toiyama Y, Mohri Y, Saigusa S, Ichikawa T, Shimura T, Yasuda H, Okita Y, Yoshiyama S, Kobayashi M, Araki T, Inoue Y, Kusunoki M (2017) Prevalence of anastomotic leak and the impact of indocyanine green fluorescein imaging for evaluating blood flow in the gastric conduit following esophageal cancer surgery. Esophagus 14:351–359CrossRefGoogle Scholar
- 7.Diana M, Agnus V, Halvax P, Liu YY, Dallemagne B, Schlagowski AI, Geny B, Diemunsch P, Lindner V, Marescaux J (2015) Intraoperative fluorescence-based enhanced reality laparoscopic real-time imaging to assess bowel perfusion at the anastomotic site in an experimental model. Br J Surg 102:e169–e176CrossRefGoogle Scholar
- 8.Diana M, Dallemagne B, Chung H, Nagao Y, Halvax P, Agnus V, Soler L, Lindner V, Demartines N, Diemunsch P, Geny B, Swanstrom L, Marescaux J (2014) Probe-based confocal laser endomicroscopy and fluorescence-based enhanced reality for real-time assessment of intestinal microcirculation in a porcine model of sigmoid ischemia. Surg Endosc 28:3224–3233CrossRefGoogle Scholar
- 9.Diana M, Halvax P, Dallemagne B, Nagao Y, Diemunsch P, Charles AL, Agnus V, Soler L, Demartines N, Lindner V, Geny B, Marescaux J (2014) Real-time navigation by fluorescence-based enhanced reality for precise estimation of future anastomotic site in digestive surgery. Surg Endosc 28:3108–3118CrossRefGoogle Scholar
- 16.Diana M, Halvax P, Pop R, Schlagowski I, Bour G, Liu YY, Legner A, Diemunsch P, Geny B, Dallemagne B, Beaujeux R, Demartines N, Marescaux J (2015) Gastric supply manipulation to modulate ghrelin production and enhance vascularization to the cardia: proof of the concept in a porcine model. Surg Innov 22:5–14CrossRefGoogle Scholar
- 17.Diana M, Noll E, Legner A, Kong SH, Liu YY, Schiraldi L, Marchegiani F, Bano J, Geny B, Charles AL, Dallemagne B, Lindner V, Mutter D, Diemunsch P, Marescaux J (2018) Impact of valve-less vs. standard insufflation on pneumoperitoneum volume, inflammation, and peritoneal physiology in a laparoscopic sigmoid resection experimental model. Surg Endosc 32:3215–3224CrossRefGoogle Scholar
- 18.Pedregosa F, Gaël V, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay É (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830Google Scholar
- 19.Devijver PA, Kittler J (1982) Pattern recognition: a statistical approach. Prentice-Hall, LondonGoogle Scholar
- 20.Ris F, Liot E, Buchs NC, Kraus R, Ismael G, Belfontali V, Douissard J, Cunningham C, Lindsey I, Guy R, Jones O, George B, Morel P, Mortensen NJ, Hompes R, Cahill RA, Near-Infrared Anastomotic Perfusion Assessment Network V (2018) Multicentre phase II trial of near-infrared imaging in elective colorectal surgery. Br J Surg 105:1359–1367CrossRefGoogle Scholar
- 21.Armstrong G, Croft J, Corrigan N, Brown JM, Goh V, Quirke P, Hulme C, Tolan D, Kirby A, Cahill R, O’Connell PR, Miskovic D, Coleman M, Jayne D (2018) IntAct: intra-operative fluorescence angiography to prevent anastomotic leak in rectal cancer surgery: a randomized controlled trial. Colorectal Dis. https://doi.org/10.1111/codi.14257 Google Scholar
- 26.Selka F, Agnus V, Nicolau S, Bessaid A, Soler L, Marescaux J, Diana M (2014) Fluorescence-based enhanced reality for colorectal endoscopic surgery. Biomedical Image Registration, London, pp 114–123Google Scholar
- 28.Diana M, Pop R, Beaujeux R, Dallemagne B, Halvax P, Schlagowski I, Liu YY, Diemunsch P, Geny B, Lindner V, Marescaux J (2015) Embolization of arterial gastric supply in obesity (EMBARGO): an endovascular approach in the management of morbid obesity. proof of the concept in the porcine model. Obes Surg 25:550–558CrossRefGoogle Scholar