Airway Skill Assessment with Spatiotemporal Attention Mechanisms Using Human Gaze

2025-06-24

AI Summary

Subjective assessment of traditional airway management skills—such as orotracheal intubation (ETI)—lacks reliability in high-stakes settings like military emergency care. To address this, we propose an objective evaluation framework integrating human gaze data with video analysis. Our method introduces a gaze-guided spatiotemporal attention mechanism for ETI skill assessment: a video autoencoder extracts spatiotemporal features, while gaze-derived visual masks dynamically localize critical procedural regions, enhancing both model interpretability and discriminative power. Experimental results demonstrate statistically significant improvements over conventional assessment methods in accuracy, sensitivity, and inter-rater consistency. The core contribution is a task-driven, interpretable visual attention paradigm that bridges perceptual behavior (gaze) with technical performance, enabling quantitative, objective measurement of procedural competence in prehospital and combat-casualty care training.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Assessing airway skills objectively using gaze data
• Improving ETI procedure recognition with attention mechanisms
• Enhancing clinical training accuracy in emergency medicine

Innovation

• Uses human gaze data for ETI assessment
• Leverages attention mechanism with visual masks
• Autoencoder and classifier for video analysis