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

ABSTRACT:

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 a bottleneck, we prospectively investigate active learning (AL) to reduce annotation effort and present automatic recognition of surgomic features.

Methods

To establish a process for development of surgomic features, ten video-based features related to bleeding, as highly relevant intraoperative complication, were chosen. They comprise the amount of blood and smoke in the surgical field, six instruments, and two anatomic structures. Annotation of selected frames from robot-assisted mini

SUBMITTER: Brandenburg JM 

PROVIDER: S-EPMC10615926 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature