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Related Experiment Video

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Image Acquisition using Portable Sonography for Emergency Airway Management
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Machine learning-based prediction of difficult laryngoscopy in infants with Pierre Robin sequence using quantitative

Danling Hu1, Weiwei Cai1, Anwen Zheng1

  • 1Department of Anesthesiology, Children's Hospital of Nanjing Medical University, Nanjing, China.

Frontiers in Neurology
|July 9, 2026
PubMed
Summary

Quantitative 3D-CT scans identify key airway predictors for difficult laryngoscopies in infants with Pierre Robin sequence (PRS). Machine learning models, particularly Extra Trees, show promise in predicting these challenging exposures.

Keywords:
Pierre Robin sequenceairway assessmentdifficult laryngoscopymachine learningpredictive modelthree-dimensional computed tomography

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Last Updated: Jul 10, 2026

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Point-of-Care Ultrasound: A Review of Ultrasound Parameters for Predicting Difficult Airways
08:21

Point-of-Care Ultrasound: A Review of Ultrasound Parameters for Predicting Difficult Airways

Published on: April 7, 2023

Area of Science:

  • Medical Imaging
  • Pediatric Airway Management
  • Computational Biology

Background:

  • Infants with Pierre Robin sequence (PRS) often face difficult laryngoscopic exposure due to craniofacial abnormalities.
  • Quantitative three-dimensional computed tomography (3D-CT) offers objective airway assessment for PRS patients.
  • Predicting laryngoscopic difficulty is crucial for optimizing surgical planning and patient outcomes.

Purpose of the Study:

  • To identify key 3D-CT parameters associated with difficult laryngoscopic exposure in infants with PRS.
  • To develop and externally validate machine learning (ML) models for predicting laryngoscopic difficulty.
  • To improve preoperative airway assessment in infants with PRS.

Main Methods:

  • Retrospective analysis of 214 infants with PRS undergoing mandibular distraction osteogenesis.
  • Classification of patients into easy (Cormack-Lehane grades I-II) and difficult (grades III-IV) laryngoscopic exposure groups.
  • Development and validation of seven ML models using quantitative 3D-CT parameters, including Extra Trees and XGBoost.

Main Results:

  • Tongue length, tongue base-posterior pharyngeal wall distance, sagittal oropharyngeal area, and tongue base-epiglottic angle were identified as independent predictors.
  • The Extra Trees model demonstrated superior generalizability in the temporal validation cohort (AUC=0.876, accuracy=0.812, F1-score=0.805).
  • The Extra Trees model showed excellent calibration and substantial net clinical benefit across a wide range of threshold probabilities.

Conclusions:

  • Quantitative 3D-CT parameters of tongue morphology and oropharyngeal dimensions are significant predictors of difficult laryngoscopic exposure in PRS infants.
  • The Extra Trees ML model shows potential for accurate and generalizable prediction of laryngoscopic difficulty in this population.
  • These findings can enhance preoperative airway assessment and surgical planning for infants with PRS.