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

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...

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Veröffentlicht in:Surgical endoscopy 2022, Vol.36 (1), p.833-843
Hauptverfasser: Adballah, Mourad, Espinel, Yamid, Calvet, Lilian, Pereira, Bruno, Le Roy, Bertrand, Bartoli, Adrien, Buc, Emmanuel
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container_start_page 833
container_title Surgical endoscopy
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creator Adballah, Mourad
Espinel, Yamid
Calvet, Lilian
Pereira, Bruno
Le Roy, Bertrand
Bartoli, Adrien
Buc, Emmanuel
description 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  
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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  &lt; 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.</description><identifier>ISSN: 0930-2794</identifier><identifier>EISSN: 1432-2218</identifier><identifier>DOI: 10.1007/s00464-021-08798-z</identifier><identifier>PMID: 34734305</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Abdominal Surgery ; Animals ; Augmented Reality ; Computer Science ; Computer Vision and Pattern Recognition ; Disease Models, Animal ; Dynamic Manuscript ; Gastroenterology ; Gynecology ; Hepatology ; Imaging, Three-Dimensional ; Laparoscopy ; Laparoscopy - methods ; Liver ; Liver cancer ; Liver Neoplasms - diagnostic imaging ; Liver Neoplasms - surgery ; Medicine ; Medicine &amp; Public Health ; Proctology ; Sheep ; Surgery ; Tumors</subject><ispartof>Surgical endoscopy, 2022, Vol.36 (1), p.833-843</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021</rights><rights>2021. 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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  &lt; 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). 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Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><jtitle>Surgical endoscopy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Adballah, Mourad</au><au>Espinel, Yamid</au><au>Calvet, Lilian</au><au>Pereira, Bruno</au><au>Le Roy, Bertrand</au><au>Bartoli, Adrien</au><au>Buc, Emmanuel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Augmented reality in laparoscopic liver resection evaluated on an ex-vivo animal model with pseudo-tumours</atitle><jtitle>Surgical endoscopy</jtitle><stitle>Surg Endosc</stitle><addtitle>Surg Endosc</addtitle><date>2022</date><risdate>2022</risdate><volume>36</volume><issue>1</issue><spage>833</spage><epage>843</epage><pages>833-843</pages><issn>0930-2794</issn><eissn>1432-2218</eissn><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  &lt; 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.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>34734305</pmid><doi>10.1007/s00464-021-08798-z</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-3559-166X</orcidid></addata></record>
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subjects Abdominal Surgery
Animals
Augmented Reality
Computer Science
Computer Vision and Pattern Recognition
Disease Models, Animal
Dynamic Manuscript
Gastroenterology
Gynecology
Hepatology
Imaging, Three-Dimensional
Laparoscopy
Laparoscopy - methods
Liver
Liver cancer
Liver Neoplasms - diagnostic imaging
Liver Neoplasms - surgery
Medicine
Medicine & Public Health
Proctology
Sheep
Surgery
Tumors
title Augmented reality in laparoscopic liver resection evaluated on an ex-vivo animal model with pseudo-tumours
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