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Augmented reality in laparoscopic liver resection evaluated on an ex-vivo animal model with pseudo-tumours

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Abstract

Background

The aim of this study was to assess the performance of our augmented reality (AR) software (Hepataug) during laparoscopic resection of liver tumours and compare it to standard ultrasonography (US).

Materials and methods

Ninety pseudo-tumours ranging from 10 to 20 mm were created in sheep cadaveric livers by injection of alginate. CT-scans were then performed and 3D models reconstructed using a medical image segmentation software (MITK). The livers were placed in a pelvi-trainer on an inclined plane, approximately perpendicular to the laparoscope. The aim was to obtain free resection margins, as close as possible to 1 cm. Laparoscopic resection was performed using US alone (n = 30, US group), AR alone (n = 30, AR group) and both US and AR (n = 30, ARUS group). R0 resection, maximal margins, minimal margins and mean margins were assessed after histopathologic examination, adjusted to the tumour depth and to a liver zone-wise difficulty level.

Results

The minimal margins were not different between the three groups (8.8, 8.0 and 6.9 mm in the US, AR and ARUS groups, respectively). The maximal margins were larger in the US group compared to the AR and ARUS groups after adjustment on depth and zone difficulty (21 vs. 18 mm, p = 0.001 and 21 vs. 19.5 mm, p = 0.037, respectively). The mean margins, which reflect the variability of the measurements, were larger in the US group than in the ARUS group after adjustment on depth and zone difficulty (15.2 vs. 12.8 mm, p < 0.001). When considering only the most difficult zone (difficulty 3), there were more R1/R2 resections in the US group than in the AR + ARUS group (50% vs. 21%, p = 0.019).

Conclusion

Laparoscopic liver resection using AR seems to provide more accurate resection margins with less variability than the gold standard US navigation, particularly in difficult to access liver zones with deep tumours.

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Acknowledgements

This study benefitted from the financial support of a 2019-2020 CNRS pre-transfer project and of the 2020-2023 cancéropôle CLARA Proof-of-Concept project AIALO.

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Authors and Affiliations

  1. Institut Pascal, UMR6602, Endoscopy and Computer Vision Group, Faculté de Médecine, Bâtiment 3C, 28 place Henri Dunant, 63000, Clermont-Ferrand, France

    Mourad Adballah, Yamid Espinel, Lilian Calvet, Bertrand Le Roy, Adrien Bartoli & Emmanuel Buc

  2. Department of Digestive and Hepatobiliary Surgery, University Hospital Clermont-Ferrand, 1 Place Lucie et Raymond Aubrac, 63003, Clermont-Ferrand Cedex, France

    Mourad Adballah & Emmanuel Buc

  3. Biostatistics Department (DRCI), University Hospital Clermont-Ferrand, 63000, Clermont-Ferrand, France

    Lilian Calvet, Bruno Pereira & Adrien Bartoli

  4. Department of Digestive and Oncologic Surgery, University Hospital Nord St-Etienne, Avenue Albert Raimond, 42270, Saint-Priest en Jarez, France

    Bertrand Le Roy

Authors
  1. Mourad Adballah
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  2. Yamid Espinel
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  4. Bruno Pereira
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  5. Bertrand Le Roy
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  7. Emmanuel Buc
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Correspondence to Emmanuel Buc.

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Mourad Adballah, Yamid Espinel, Lilian Calvet, Bruno Pereira, Bertrand Le Roy, Adrien Bartoli, Emmanuel Buc have no conflicts of interest or financial ties to disclose.

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Adballah, M., Espinel, Y., Calvet, L. et al. Augmented reality in laparoscopic liver resection evaluated on an ex-vivo animal model with pseudo-tumours. Surg Endosc 36, 833–843 (2022). https://doi.org/10.1007/s00464-021-08798-z

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  • DOI: https://doi.org/10.1007/s00464-021-08798-z

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