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AI-Assisted Video Monitoring for Tracheostomy-Dependent Infants: A Proof-Of-Concept Study

Colleen F Cecola1, Christine Settoon1, Lauren S Buck1

  • 1Louisiana State University Health Sciences Center, New Orleans, Louisiana, USA.

The Laryngoscope
|June 5, 2026
PubMed

Insights

Patient-personalized AI video analysis accurately detects tracheostomy tube status and infant distress. This AI system shows promise for real-world clinical use after further evaluation.

Area of Science:

  • Artificial Intelligence in Medicine
  • Pediatric Otolaryngology
  • Medical Imaging Analysis

Background:

  • Tracheostomy-dependent infants require continuous monitoring for tube status and distress.
  • Current monitoring methods may be limited in detecting subtle changes.
  • AI-assisted video analysis offers a novel approach to objective patient assessment.

Purpose of the Study:

  • To evaluate the performance of Eyes-On, an AI system for detecting tracheostomy tube status and visual distress in infants.
  • To assess the effectiveness of patient-personalized AI calibration for improved accuracy.
  • To determine the system's potential for frame-level classification in clinical settings.

Main Methods:

  • Retrospective analysis of video recordings from 25 tracheostomy-dependent infants.
  • Development of a YOLOv11 detector for cannulation status and a facial distress classifier.
  • Training and evaluation of generalized pretrained models, followed by patient-specific calibration.
  • Utilizing decision curve analysis for individualized threshold selection.

Main Results:

  • Calibrated AI significantly improved cannulation detection accuracy to 0.940 (sensitivity 0.997) and distress classification accuracy to 0.960 (sensitivity 0.974).
  • Pretrained models showed moderate performance, with significant gains after patient-specific calibration.
  • The system demonstrated high performance in frame-level classification after individualized adjustments.

Conclusions:

  • Patient-personalized AI video analysis shows potential for accurate, frame-level detection of tracheostomy status and infant facial distress.
  • The study highlights the importance of within-patient calibration for AI system performance.
  • Prospective real-world evaluation is necessary to validate clinical utility and alarm capabilities.
Abstract

Keywords:
artificial intelligencecomputer visioninfant monitoringtracheostomy

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