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NCT Number: NCT06982144

AI-based Prediction Model of Difficult Tracheal Intubation Using Medical Image Parameters

Difficult airway is a life-threatening event during anesthesia. Prediction model is helpful to detect high-risk patients and decrease the risk of un-anticipated difficult airway. Present models are usually based on Mallampati grade and the width of mouth open. However, the prediction accuracy is only about 0.7-0.8 in different populations. Present study is designed to investigate if AI-based prediction model using medical imaging parameters (such as CT and MRI) can increase the accuracy of prediction model.

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Key information

Conditions

Difficult Airway

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • age ≥18 years old;
  • surgical patients undergoing general anesthesia with endotracheal intubation;
  • with head and neck CT examination results
  • Consent to participate in the study.

Exclusion criteria

  • The presence of laryngeal edema;
  • The presence of airway stenosis, including internal airway stenosis (such as foreign body or tumor) or stenosis caused by external tracheal mass compression;
  • tracheo-esophageal fistula;
  • severe gastroesophageal reflux;
  • previous upper airway surgery, such as laryngeal cancer radical surgery, snoring surgery, etc.

6）participating in other research projects

Treatment and study plan

Primary outcomes

  1. The accuracy of prediction model based on AI analysis of medical imaging parameters

    Time frame: day 1 (From enrollment to the end of anesthesia induction)

    To establish a prediction model for difficult tracheal intubation based on medical imaging parameters (such as CT and MRI) using AI algorithms and verify its predictive accuracy.

Study contacts

Contact information is provided by the study sponsor or research team.

Dongliang Mu Associate professor

CONTACT

[email protected]

+86 13810702725

Sponsors and collaborators

Lead sponsor

Mu Dong Liang

Other

Registry information

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
May 21, 2025
Registry last updated
May 21, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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