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Improving vision for surgeons during laparoscopy: the Enhanced Laparoscopic Vision System (ELViS)

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Abstract

Background

For many abdominal surgical interventions, laparotomy has gradually been replaced by laparoscopy, with numerous benefits for the patient in terms of post-operative recovery. However, during laparoscopy, the endoscope only provides a single viewpoint to the surgeon, leaving numerous blind spots and opening the way to peri-operative adverse events. Alternative camera systems have been proposed, but many lack the requisite resolution/robustness for use during surgery or cannot provide real-time images. Here, we present the added value of the Enhanced Laparoscopic Vision System (ELViS) which overcomes these limitations and provides a broad view of the surgical field in addition to the usual high-resolution endoscope.

Methods

Experienced laparoscopy surgeons performed several typical procedure steps on a live pig model. The time-to-completion for surgical exercises performed by conventional endoscopy and ELViS-assisted surgery was measured. A debriefing interview following each operating session was conducted by an ergonomist, and a System Usability Scale (SUS) score was determined.

Results

Proof of concept of ELVIS was achieved in an animal model with seven expert surgeons without peroperative adverse events related to the surgical device. No differences were found in time-to-completion. Mean SUS score was 74.7, classifying the usability of the ELViS as “good”. During the debriefing interview, surgeons highlighted several situations where the ELViS provided a real advantage (such as during instrument insertion, exploration of the abdominal cavity or for orientation during close work) and also suggested avenues for improvement of the system.

Conclusions

This first test of the ELViS prototype on a live animal model demonstrated its usability and provided promising and useful feedback for further development.

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Change history

  • 12 March 2021

    This article was updated to correct Affiliation 5: “Grenoble University Hospital,” not “University Grenoble Alpes.”

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Acknowledgements

The authors would like to thank Prof. Jean Luc Bosson and Mr Aboubacar SISSOKO (data stat cell, CHU Grenoble) for their expertise and contribution to the statistical analysis of the data, and Maighread Gallagher-Gambarelli (TWS Scientific Editing) for suggestions on English usage in the paper. The authors would also like to thank the French National Research Agency for funding this work through the DEPORRA2 project—reference ANR-14-CE17-0009, the CQFD LABCOM project—reference ANR-13-LAB3-0002, and the Investissements d'Avenir programme (Labex CAMI—reference ANR-11-LABX-0004).

Funding

The work described in this paper was funded by the French National Agency for Research (Agence Nationale de la Recherche) as part of the DEPORRA2 project (reference ANR-14-CE17-0009) and by the French government’s Investissements d'Avenir programme as part of the Labex CAMI (reference ANR-11-LABX-0004).

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Correspondence to Bertrand Trilling.

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Dr. Sandrine Voros has registered a patent EP2903495B1 (FR2996437B1). The work described in this paper was funded by the French National Agency for Research (Agence Nationale de la Recherche) as part of the DEPORRA2 project (Reference ANR-14-CE17-0009), by the CQFD LABCOM project (Reference ANR-13-LAB3-0002), and by the French government’s Investissements d'Avenir programme as part of the Labex CAMI (Reference ANR-11-LABX-0004). Sandrine Voros, Bertrand Trilling, Adrian Mancini, Gaëlle Fiard, Pierre Alain Barraud, Marion Decrouez, Sinara Vijayan, Mathias Tummers, Jean Luc Faucheron, Sophie Silvent, Christel Schwartz have no other conflicts of interest or financial ties to disclose.

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Trilling, B., Mancini, A., Fiard, G. et al. Improving vision for surgeons during laparoscopy: the Enhanced Laparoscopic Vision System (ELViS). Surg Endosc 35, 2403–2415 (2021). https://doi.org/10.1007/s00464-021-08369-2

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Keywords

  • Distributed laparoscopy
  • Trocar prototype
  • Animal model
  • Phase 0 medical device evaluation
  • Enhanced visualisation