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Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study.

Jae-Geum ShimKyoung-Ho RyuSung Hyun LeeEun-Ah ChoSungho LeeJin Hee Ahn
Published in: PloS one (2021)
In this study, the machine learning models predicted the optimal tracheal tube tip location for pediatric patients more accurately than the formula-based methods. Machine learning models using biometric variables may help clinicians make decisions regarding optimal tracheal tube depth in pediatric patients.
Keyphrases
  • machine learning
  • artificial intelligence
  • big data
  • optical coherence tomography
  • deep learning
  • human milk