4.6 Article

A case series study of augmented reality in laparoscopic liver resection with a deformable preoperative model

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SPRINGER
DOI: 10.1007/s00464-020-07815-x

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Laparoscopy; Liver; Resection; Augmented reality; Deformable 3D model; Overlay

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Background Previous work in augmented reality (AR) guidance in monocular laparoscopic hepatectomy requires the surgeon to manually overlay a rigid preoperative model onto a laparoscopy image. This may be fairly inaccurate because of significant liver deformation. We have proposed a technique which overlays adeformablepreoperative model semi-automatically onto a laparoscopic image using a new software calledHepataug. The aim of this study is to show the feasibility ofHepataugto perform AR with a deformable model in laparoscopic hepatectomy. Methods We ranHepataugduring the procedures, as well as the usual means of laparoscopic ultrasonography (LUS) and visual inspection of the preoperative CT or MRI. The primary objective was to assess the feasibility ofHepataug, in terms of minimal disruption of the surgical workflow. The secondary objective was to assess the potential benefit ofHepataug, by subjective comparison with LUS. Results From July 2017 to March 2019, 17 consecutive patients were included in this study. AR was feasible in all procedures, with good correlation with LUS. However, for 2 patients, LUS did not reveal the location of the tumors.Hepatauggave a prediction of the tumor locations, which was confirmed and refined by careful inspection of the preoperative CT or MRI. Conclusion Hepataugshowed a minimal disruption of the surgical workflow and can thus be feasibly used in real hepatectomy procedures. Thanks to its new mechanism of semi-automatic deformable alignment,Hepataugalso showed a good agreement with LUS and visual CT or MRI inspection in subsurface tumor localization. Importantly,Hepataugyields reproducible results. It is easy to use and could be deployed in any existing operating room. Nevertheless, comparative prospective studies are needed to study its efficacy.

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