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

NCT06982144 | Not Yet Recruiting

• 1 other identifier: 2025R-0037
• Study Type: observational
• Target: 228
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Difficult Airway

Keywords

difficult airway; AI-based method; medical imaging; prediction model

Outcome Measures

Primary Outcomes (1)

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

  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.

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

Study Arms (1)

Adult patients scheduled for selective surgery

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with head and neck CT data undergoing surgery under general anesthesia with endotracheal intubation

You may qualify if:

• 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.

You may not qualify if:

• 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.
• ）participating in other research projects

Sponsors & Collaborators

• Mu Dong Liang: lead

Study Sites (1)

Peking University First Hospital

Beijing, Beijing Municipality, 100034, China

Location

Central Study Contacts

Dongliang Mu Associate professor

CONTACT

+86 13810702725; mudongliang@bjmu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: May 13, 2025
• First Posted: May 21, 2025
• Study Start: May 20, 2025
• Primary Completion: March 1, 2026
• Study Completion (Estimated): May 30, 2026
• Last Updated: May 21, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)