Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study - Brandenburg, Johanna; Jenke, Alexander; Stern, Antonia; Schulze, André; Younis, Rayan; Petrynowski, Philipp; Davitashvili, Tornike; Vanat, Vincent; Bhasker, Nithya; Schneider, Sophia; Mündermann, Lars; Reinke, Annika; et al. (Surgical endoscopy and other interventional techniques, 2023)
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.