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Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study

With Surgomics, we aim for personalized prediction of the patient's surgical outcome using machine-learning (ML) on multimodal intraoperative data to extract surgomic features as surgical process characteristics. As high-quality annotations by medical experts are crucial, but still a bottleneck...

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Main Authors: Brandenburg, Johanna (Author) , Jenke, Alexander (Author) , Stern, Antonia (Author) , Schulze, André (Author) , Younis, Rayan (Author) , Petrynowski, Philipp (Author) , Davitashvili, Tornike (Author) , Vanat, Vincent (Author) , Bhasker, Nithya (Author) , Schneider, Sophia (Author) , Mündermann, Lars (Author) , Reinke, Annika (Author) , Kolbinger, Fiona (Author) , Jörns, Vanessa (Author) , Fritz-Kebede, Fleur (Author) , Dugas, Martin (Author) , Maier-Hein, Lena (Author) , Klotz, Rosa (Author) , Distler, Marius (Author) , Weitz, Jürgen (Author) , Müller, Beat P. (Author) , Speidel, Stefanie (Author) , Bodenstedt, Sebastian (Author) , Wagner, Martin (Author)
Other Authors: Daum, Marie T. J. (Other)
Format: Article (Journal)
Language:English
Published: 14 October 2023
In: Surgical endoscopy and other interventional techniques
Year: 2023, Volume: 37, Issue: 11, Pages: 8577-8593
ISSN:1432-2218
DOI:10.1007/s00464-023-10447-6
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s00464-023-10447-6
Verlag, kostenfrei, Volltext: https://link.springer.com/article/10.1007/s00464-023-10447-6
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Author Notes:Johanna M. Brandenburg, Alexander C. Jenke, Antonia Stern, Marie T.J. Daum, André Schulze, Rayan Younis, Philipp Petrynowski, Tornike Davitashvili, Vincent Vanat, Nithya Bhasker, Sophia Schneider, Lars Mündermann, Annika Reinke, Fiona R. Kolbinger, Vanessa Jörns, Fleur Fritz-Kebede, Martin Dugas, Lena Maier-Hein, Rosa Klotz, Marius Distler, Jürgen Weitz, Beat P. Müller-Stich, Stefanie Speidel, Sebastian Bodenstedt, Martin Wagner
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Summary:With Surgomics, we aim for personalized prediction of the patient's surgical outcome using machine-learning (ML) on multimodal intraoperative data to extract surgomic features as surgical process characteristics. As high-quality annotations by medical experts are crucial, but still a bottleneck, we prospectively investigate active learning (AL) to reduce annotation effort and present automatic recognition of surgomic features.
Item Description:Gesehen am 05.03.2024
Physical Description:Online Resource
ISSN:1432-2218
DOI:10.1007/s00464-023-10447-6

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