Exploration and Application of Intelligent Difficult Airway Assessment Scheme

NCT ID: NCT06626204

Last Updated: 2024-10-04

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 430 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-09-23
• Study Completion Date: 2025-12-31

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.

Detailed Description

The research background is based on a critical issue in anesthetic practice - the management of difficult airways. A difficult airway refers to the challenges encountered during mask ventilation or endotracheal intubation, which can lead to severe complications or even death. The American Society of Anesthesiologists (ASA) defines a difficult airway as one that presents difficulties in mask ventilation, laryngoscopy, use of supraglottic airway devices, endotracheal intubation, extubation, or invasive airway access, including but not limited to situations that are anticipated or unanticipated. Studies have shown that the incidence of difficult and unventilatable mask airways in the general subject population is 2.2% and 0.15%, respectively, while the rate of difficult or failed intubation in general anesthesia events is between 0.43% and 0.52%. These data highlight the importance of accurate airway assessment before anesthesia.

In China, due to the large population, there is a significant demand for medical and health resources, especially in the fields of surgery and anesthesia. The diversity of China's population, including regional, ethnic, and genetic background differences, may affect airway structure, thereby influencing airway management strategies. Therefore, conducting research on the assessment and management of difficult airways in China has important clinical and social significance.

Preoperative airway assessment is a key step in preventing complications associated with difficult airways. Currently, the modified Mallampati classification and the Cormack-Lehane grading are commonly used assessment tools in clinical practice. The modified Mallampati classification assesses the difficulty of the airway by observing the visibility of structures within the oral cavity, while the Cormack-Lehane grading assesses it through the visibility of laryngeal structures during direct laryngoscopy. Although these methods are widely used in clinical practice, they have issues with subjectivity, poor repeatability, and limited accuracy.

With the advancement of medical technology, especially the rapid development of artificial intelligence (AI) and telemedicine, new opportunities have emerged to improve the assessment methods for difficult airways. The use of intelligent devices and algorithms can provide more precise measurements and analysis of the subject's airway anatomy, thereby improving the accuracy and reliability of the assessment. This new method not only reduces the interference of subjectivity but also improves the repeatability of the assessment through standardized image collection and analysis processes.

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.

The clinical significance of the study lies in the fact that, with more accurate airway assessment, anesthesiologists can take appropriate preventive measures and strategies in advance, reducing complications due to improper airway management, such as hypoxia, failed intubation, etc. This will directly improve surgical safety, reduce the postoperative recovery time and medical costs for subjects. In addition, the results of the study may also provide new theoretical bases and practical guidelines for the field of airway assessment, promoting the development and innovation of related technologies and methods.

The social value of conducting such research in China is that, with the trend of digital transformation in the medical industry, the application of intelligent airway assessment methods helps to improve overall medical efficiency and subject safety. In areas with limited resources or remote geographical locations, doctors can use remote connections and intelligent devices to assess the airways of subjects, achieving high-quality medical services.

Conditions

Difficult Airway

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: PROSPECTIVE

Eligibility Criteria

Inclusion Criteria

• 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.

Exclusion Criteria

• 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.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Min Su

OTHER

Sponsor Role: lead

Responsible Party

Min Su

MD

Responsibility Role: SPONSOR_INVESTIGATOR

Principal Investigators

Min Su

Role: STUDY_DIRECTOR

First Affiliated Hospital of Chongqing Medical University

Locations

First Affiliated Hospital of Chongqing Medical University

Chongqing, Chongqing Municipality, China

Site Status: RECRUITING

Countries

China

Central Contacts

Wenjie cheng

Role: CONTACT

2534493622@qq.com

17815370965

Facility Contacts

Wenjie Cheng

Role: primary

2534493622@qq.com

17815370965

Other Identifiers

202409701

Identifier Type: -

Identifier Source: org_study_id