Artificial intelligence (AI)-based pose estimation detects movements linked to unplanned tube removal in ICU patients

Abstract

Unplanned removal of life-sustaining tubes in intensive care units (ICUs) poses serious risks, yet existing monitoring methods relying on physical restraints have ethical and clinical drawbacks. Here we applied artificial intelligence (AI)-based pose estimation using MediaPipe to analyze ICU surveillance videos, extracting skeletal coordinates to detect movements associated with tube removal. Using Singular Spectrum Transformation for change-point detection, we identified movement changes corresponding to tube-removal behaviors in three consented cases, achieving average precision values substantially above chance. These preliminary results demonstrate that AI-driven, contactless motion analysis can capture clinically relevant signals from existing ICU infrastructure without additional patient burden. Although limited by sample size and environmental factors, this approach holds promise for real-time, non-invasive monitoring to reduce reliance on physical restraints and enhance patient safety in critical care settings.