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

Mu Dong Liang

Status: Not yet enrolling

Conditions: Difficult Airway

Study type: Observational

Funder types: Other

Identifiers

NCT06982144

2025R-0037

Details and patient eligibility

About

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.

Enrollment: 228 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

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

Exclusion criteria

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

6）participating in other research projects

Trial design

228 participants in 1 patient group

Adult patients scheduled for selective surgery