Deep learning-based automatic surgical step recognition in intraoperative videos for transanal total mesorectal excision.

ABSTRACT:

Background

Dividing a surgical procedure into a sequence of identifiable and meaningful steps facilitates intraoperative video data acquisition and storage. These efforts are especially valuable for technically challenging procedures that require intraoperative video analysis, such as transanal total mesorectal excision (TaTME); however, manual video indexing is time-consuming. Thus, in this study, we constructed an annotated video dataset for TaTME with surgical step information and evaluated the performance of a deep learning model in recognizing the surgical steps in TaTME.

Methods

This was a single-institutional retrospective feasibility study. All TaTME intraoperative videos were divided into frames. Each frame was manually annotated as one of the following major steps:

SUBMITTER: Kitaguchi D 

PROVIDER: S-EPMC8758657 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature