Deep learning-based recognition of key anatomical structures during robot-assisted minimally invasive esophagectomy.

ABSTRACT:

Objective

To develop a deep learning algorithm for anatomy recognition in thoracoscopic video frames from robot-assisted minimally invasive esophagectomy (RAMIE) procedures using deep learning.

Background

RAMIE is a complex operation with substantial perioperative morbidity and a considerable learning curve. Automatic anatomy recognition may improve surgical orientation and recognition of anatomical structures and might contribute to reducing morbidity or learning curves. Studies regarding anatomy recognition in complex surgical procedures are currently lacking.

Methods

Eighty-three videos of consecutive RAMIE procedures between 2018 and 2022 were retrospectively collected at University Medical Center Utrecht. A surgical PhD candidate and an expert surgeon annotated t

SUBMITTER: den Boer RB 

PROVIDER: S-EPMC10322962 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature