Exploration and Application of Intelligent Difficult Airway Assessment Scheme

NCT06626204 | Completed

• 1 other identifier: 202409701
• Study Type: observational
• Target: 475
• Locations: 1 country
• Sites: 1

Brief Summary

The study aims to explore the effectiveness of an intelligent difficult airway assessment protocol and its potential in clinical applications. The management of difficult airways is a critical task in anesthesiology, and poor management can lead to severe complications or even death. The American Society of Anesthesiologists defines a difficult airway as one that presents difficulties in mask ventilation or endotracheal intubation. Previous studies have shown that the incidence of difficult airways is not low in patients undergoing general anesthesia, emphasizing the need for optimization of airway management strategies. Preoperative airway assessment is an essential step in preventing complications associated with difficult airways. Currently, the modified Mallampati classification and the Cormack-Lehane grading are two commonly used assessment tools. However, these methods rely on the subjective judgment of clinicians and may have limitations in accuracy and consistency. With the development of artificial intelligence and telemedicine technologies, new assessment methods have become possible, offering more precise measurements and analysis of airway anatomy. This study proposes an intelligent airway assessment system that combines phonation modulation and tongue position adjustment, aiming to improve the accuracy and reliability of assessments. The system uses deep learning algorithms to analyze oral images of subjects to predict airway difficulty. The study will also explore the correlation of this system with traditional assessment methods and establish a predictive model for difficult airways. As a country with a large population, China has a significant demand for medical and health resources, especially in the fields of surgery and anesthesia. The diversity of China's population may affect airway structure, thereby influencing airway management strategies. Therefore, conducting such research in China has important clinical significance and social value.

Conditions

Difficult Airway

Keywords

Difficult Airway; artificial intelligence; Modified Mallampati Classification

Outcome Measures

Primary Outcomes (1)

• Correlation between intelligent airway assessment and modification of Mallampati classification and Cormack-Lehane grading.

  From enrollment to one day after extubation.

Eligibility Criteria

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

Study Population

Patients scheduled for elective general anesthesia with endotracheal intubation.

You may qualify if:

• Age ≥18 years, with no gender restrictions;
• Subjects who are scheduled to undergo elective general anesthesia and require endotracheal intubation;
• Subjects classified as American Society of Anesthesiologists Physical Status (ASA-PS) Class I, II, and III;
• Volunteers who are willing to participate in this clinical trial and have signed the Informed Consent Form.

You may not qualify if:

• Subjects with known airway deformities, tumors, or other structural abnormalities that may affect airway assessment;
• Subjects with psychiatric disorders or other conditions that prevent cooperation;
• Pregnant or lactating women;
• Subjects who have participated in other interventional clinical trials within 1 month prior to the start of this trial;
• Subjects deemed inappropriate to participate in this clinical trial by the investigator.

Sponsors & Collaborators

• Min Su: lead

Study Sites (1)

First Affiliated Hospital of Chongqing Medical University

Chongqing, Chongqing Municipality, 400016, China

Location

Biospecimen

Retention: SAMPLES WITHOUT DNA

Modified Mallampati classification, Cormack-Lehane grading, thyromental distance, interincisal distance, and cervical mobility involved with the patient.

Study Officials

• Min Su

  First Affiliated Hospital of Chongqing Medical University

  STUDY DIRECTOR

Study Design

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

Study Record Dates

• First Submitted: October 2, 2024
• First Posted: October 3, 2024
• Study Start: September 23, 2024
• Primary Completion: January 10, 2025
• Study Completion: January 10, 2025
• Last Updated: February 18, 2026

Record last verified: 2026-02

Locations

CN (1)