Airway Skill Assessment with Spatiotemporal Attention Mechanisms Using Human Gaze

TL;DR

This paper introduces a novel machine learning approach that uses human gaze data and attention mechanisms to objectively assess airway management skills, specifically endotracheal intubation, improving accuracy and reliability.

Contribution

It is the first to incorporate human gaze data into an attention-based model for ETI skill assessment, enhancing performance over traditional methods.

Findings

Improved prediction accuracy and sensitivity.

Enhanced model trustworthiness.

Robust assessment in high-stress environments.

Abstract

Airway management skills are critical in emergency medicine and are typically assessed through subjective evaluation, often failing to gauge competency in real-world scenarios. This paper proposes a machine learning-based approach for assessing airway skills, specifically endotracheal intubation (ETI), using human gaze data and video recordings. The proposed system leverages an attention mechanism guided by the human gaze to enhance the recognition of successful and unsuccessful ETI procedures. Visual masks were created from gaze points to guide the model in focusing on task-relevant areas, reducing irrelevant features. An autoencoder network extracts features from the videos, while an attention module generates attention from the visual masks, and a classifier outputs a classification score. This method, the first to use human gaze for ETI, demonstrates improved accuracy and efficiency over traditional methods. The integration of human gaze data not only enhances model performance but also offers a robust, objective assessment tool for clinical skills, particularly in high-stress environments such as military settings. The results show improvements in prediction accuracy, sensitivity, and trustworthiness, highlighting the potential for this approach to improve clinical training and patient outcomes in emergency medicine.