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Discrimination between arterial and venous bowel ischemia by computer-assisted analysis of the fluorescent signal

  • Giuseppe Quero
  • Alfonso Lapergola
  • Manuel Barberio
  • Barbara Seeliger
  • Paola Saccomandi
  • Ludovica Guerriero
  • Didier Mutter
  • Alend Saadi
  • Marc Worreth
  • Jacques Marescaux
  • Vincent Agnus
  • Michele DianaEmail author
2018 SAGES Oral

Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

The computer-assisted dynamic analysis of the fluorescence signal enables the discrimination between different bowel ischemia models.

Keywords

Fluorescence angiography Fluorescence-based Enhanced Reality Computer-assisted analysis of fluorescence signal Tissue perfusion Machine learning 

Notes

Acknowledgements

Authors are grateful to Christopher Burel, professional in Medical English proofreading for his assistance with the manuscript revision.

Funding

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

Disclosures

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.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Giuseppe Quero
    • 1
  • Alfonso Lapergola
    • 2
  • Manuel Barberio
    • 1
  • Barbara Seeliger
    • 1
  • Paola Saccomandi
    • 1
  • Ludovica Guerriero
    • 1
  • Didier Mutter
    • 1
    • 2
    • 3
  • Alend Saadi
    • 4
  • Marc Worreth
    • 4
  • Jacques Marescaux
    • 1
    • 2
  • Vincent Agnus
    • 2
  • Michele Diana
    • 1
    • 2
    • 3
    • 4
    Email author
  1. 1.IHU-Strasbourg, Institute of Image-Guided SurgeryStrasbourgFrance
  2. 2.IRCAD, Research Institute against Cancer of the Digestive SystemStrasbourgFrance
  3. 3.Department of General, Digestive and Endocrine SurgeryUniversity Hospital of StrasbourgStrasbourgFrance
  4. 4.Department of General SurgeryHospital of PourtalèsNeuchâtelSwitzerland

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