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

BACKGROUND: 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...

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Detalles Bibliográficos
Autores principales: Brandenburg, Johanna M., Jenke, Alexander C., Stern, Antonia, Daum, Marie T. J., Schulze, André, Younis, Rayan, Petrynowski, Philipp, Davitashvili, Tornike, Vanat, Vincent, Bhasker, Nithya, Schneider, Sophia, Mündermann, Lars, Reinke, Annika, Kolbinger, Fiona R., Jörns, Vanessa, Fritz-Kebede, Fleur, Dugas, Martin, Maier-Hein, Lena, Klotz, Rosa, Distler, Marius, Weitz, Jürgen, Müller-Stich, Beat P., Speidel, Stefanie, Bodenstedt, Sebastian, Wagner, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
2023 EAES Oral
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10615926/
https://www.ncbi.nlm.nih.gov/pubmed/37833509
http://dx.doi.org/10.1007/s00464-023-10447-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10615926/
https://www.ncbi.nlm.nih.gov/pubmed/37833509
http://dx.doi.org/10.1007/s00464-023-10447-6

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