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Robotically assisted augmented reality system for identification of targeted lymph nodes in laparoscopic gynecological surgery: a first step toward the identification of sentinel node

Augmented reality in gynecological surgery

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Abstract

Background

To prove feasibility of multimodal and temporal fusion of laparoscopic images with preoperative computed tomography scans for a real-time in vivo-targeted lymph node (TLN) detection during minimally invasive pelvic lymphadenectomy and to validate and enable such guidance for safe and accurate sentinel lymph node dissection, including anatomical landmarks in an experimental model.

Methods

A measurement campaign determined the most accurate tracking system (UR5-Cobot versus NDI Polaris). The subsequent interventions on two pigs consisted of an identification of artificial TLN and anatomical landmarks without and with augmented reality (AR) assistance. The AR overlay on target structures was quantitatively evaluated. The clinical relevance of our system was assessed via a questionnaire completed by experienced and trainee surgeons.

Results

An AR-based robotic assistance system that performed real-time multimodal and temporal fusion of laparoscopic images with preoperative medical images was developed and tested. It enabled the detection of TLN and their surrounding anatomical structures during pelvic lymphadenectomy. Accuracy of the CT overlay was > 90%, with overflow rates < 6%. When comparing AR to direct vision, we found that scores were significatively higher in AR for all target structures. AR aided both experienced surgeons and trainees, whether it was for TLN, ureter, or vessel identification.

Conclusion

This computer-assisted system was reliable, safe, and accurate, and the present achievements represent a first step toward a clinical study.

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Funding

This work was supported by the French state funds managed within the “Plan Investissements d’Avenir” and by the ANR (reference ANR-10-IAHU-02).

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Authors and Affiliations

  1. Department of Gynecologic Surgery, University Hospitals of Strasbourg, Avenue Molière, 67200, Strasbourg, France

    Lise Lecointre, Massimo Lodi & Chérif Akladios

  2. Insitute of Image-Guided Surgery, IHU-Strasbourg (Institut Hospitalo-Universitaire), Strasbourg, France

    Lise Lecointre, Juan Verde, Laurent Goffin, Aïna Venkatasamy, Barbara Seeliger, Lee L. Swanström & Benoît Gallix

  3. ICube UMR 7357 - Laboratoire des Sciences de L’ingénieur, de L’informatique et de L’imagerie, CNRS, Université de Strasbourg, Strasbourg, France

    Lise Lecointre, Laurent Goffin, Barbara Seeliger & Benoît Gallix

  4. Streinth Lab (Stress Response and Innovative Therapies), Inserm UMR_S 1113 IRFAC, Interface Recherche Fondamental et Appliquée À la Cancérologie, Strasbourg, France

    Aïna Venkatasamy

  5. Department of Radiology Medical Physics, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany

    Aïna Venkatasamy

  6. Department of General, Digestive, and Endocrine Surgery, University Hospitals of Strasbourg, Strasbourg, France

    Barbara Seeliger

  7. Department of Diagnostic Radiology, McGill University, Montreal, Canada

    Benoît Gallix

Authors
  1. Lise Lecointre
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  2. Juan Verde
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  3. Laurent Goffin
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  8. Chérif Akladios
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Corresponding author

Correspondence to Lise Lecointre.

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Disclosures

Lise Lecointre, Juan Verde, Laurent Goffin, Aïna Venkatasamy, Barbara Seeliger, Massimo Lodi, Lee L Swanström, Chérif Akladios, and Benoît Gallix declare no conflict of interest.

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Lecointre, L., Verde, J., Goffin, L. et al. Robotically assisted augmented reality system for identification of targeted lymph nodes in laparoscopic gynecological surgery: a first step toward the identification of sentinel node. Surg Endosc 36, 9224–9233 (2022). https://doi.org/10.1007/s00464-022-09409-1

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  • DOI: https://doi.org/10.1007/s00464-022-09409-1

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