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Related Concept Videos

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Airway management is essential in emergency and surgical medicine, ensuring ventilation and oxygenation in patients who cannot maintain their own airway. Clinicians use a range of techniques and devices to secure the airway, depending on the patient’s condition and the clinical context. Key methods include endotracheal intubation, rapid sequence intubation (RSI), supraglottic airway devices, and advanced visualization aids. In cases where these approaches fail, surgical airway interventions are...
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Related Experiment Video

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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.

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|June 5, 2026
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Summary

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.

Keywords:
artificial intelligencecomputer visioninfant monitoringtracheostomy

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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.