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Improving difficult direct laryngoscopy prediction using deep learning and minimal image analysis: a single-center

Jong-Ho Kim1, Hee-Sun Jung2, So-Eun Lee3

  • 1Division of Big Data and Artificial Intelligence, Institute of New Frontier Research, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, 24253, Republic of Korea.

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An AI model using smartphone pictures can predict difficult direct laryngoscopy (DDL). This tool aids airway management by analyzing facial and neck images, improving patient safety.

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Area of Science:

  • Anesthesiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Predicting difficult direct laryngoscopy (DDL) is crucial for patient safety during airway management.
  • Current methods for predicting DDL may be invasive or time-consuming.
  • A non-invasive, easily accessible method for DDL prediction is needed.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI) model for predicting DDL.
  • To utilize smartphone-captured facial and neck images for DDL prediction.
  • To assess the model's performance and identify key predictive features.

Main Methods:

  • A prospective, single-center study included adult patients undergoing endotracheal intubation.
  • A deep learning model (EfficientNet-B5) was trained on smartphone images (frontal, lateral, neck extension, open mouth views).
  • Multitask learning incorporated picture view information; model performance was evaluated using AUC and F1-score.

Main Results:

  • The AI model achieved a receiver operating characteristic area under the curve (AUC) of 0.81-0.88.
  • The model's F1-score ranged from 0.72-0.81 for difficult direct laryngoscopy prediction.
  • Analysis indicated that neck and chin features in frontal and lateral views significantly contribute to prediction accuracy.

Conclusions:

  • A deep learning model using smartphone images can effectively predict difficult direct laryngoscopy.
  • The AI model offers a practical, non-invasive, and easily implementable approach to airway management assessment.
  • Incorporating multiple image views enhances the predictive performance of the AI model.