AURA: Development and Validation of an Augmented Unplanned Removal Alert System using Synthetic ICU Videos

TL;DR

AURA is a synthetic video-based system for real-time detection of unplanned extubation risks in ICUs, addressing privacy concerns and enabling safe, reproducible patient safety monitoring.

Contribution

This work introduces a fully synthetic ICU video dataset and a vision-based risk detection system for unplanned extubation, pioneering privacy-preserving ICU monitoring methods.

Findings

High accuracy in collision detection

Moderate performance in agitation recognition

Synthetic data validated as realistic by experts

Abstract

Unplanned extubation (UE) remains a critical patient safety concern in intensive care units (ICUs), often leading to severe complications or death. Real-time UE detection has been limited, largely due to the ethical and privacy challenges of obtaining annotated ICU video data. We propose Augmented Unplanned Removal Alert (AURA), a vision-based risk detection system developed and validated entirely on a fully synthetic video dataset. By leveraging text-to-video diffusion, we generated diverse and clinically realistic ICU scenarios capturing a range of patient behaviors and care contexts. The system applies pose estimation to identify two high-risk movement patterns: collision, defined as hand entry into spatial zones near airway tubes, and agitation, quantified by the velocity of tracked anatomical keypoints. Expert assessments confirmed the realism of the synthetic data, and performance evaluations showed high accuracy for collision detection and moderate performance for agitation recognition. This work demonstrates a novel pathway for developing privacy-preserving, reproducible patient safety monitoring systems with potential for deployment in intensive care settings.