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

Status

Not yet recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06982144

Enrollment

228

Registered

2025-05-21

Start date

2025-05-20

Completion date

2026-05-30

Last updated

2025-05-21

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Difficult Airway

Keywords

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

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

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

Design outcomes

Primary

Measure	Time frame	Description
The accuracy of prediction model based on AI analysis of medical imaging parameters	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.

Countries

China

Contacts

Primary ContactDongliang Mu Associate professor

mudongliang@bjmu.edu.cn+86 13810702725

Outcome results