Deep learning for video-based assessment of endotracheal intubation skills.

ABSTRACT:

Background

Endotracheal intubation (ETI) is an emergency procedure performed in civilians and combat casualty care settings to establish an airway. It's crucial that healthcare personnel are proficient in these skills, which traditionally have been evaluated through direct feedback from experts. Unfortunately, this method can be inconsistent and subjective, requiring considerable time and resources.

Methods

This study introduces a system for assessing ETI skills using video analysis. The system employs advanced video processing techniques, including a 2D convolutional autoencoder (AE) based on a self-supervision model, capable of recognizing complex patterns in videos. A 1D convolutional model enhanced with a cross-view attention module then uses AE features to make assessme

SUBMITTER: Ainam JP 

PROVIDER: S-EPMC11997077 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature