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NCT06626204
Exploration and Application of Intelligent Difficult Airway Assessment Scheme
trial in Difficult Airway in 475 participants. Completed in 10 January 2025.
10 January 2025
Quick facts
| Lead sponsor | Min Su |
|---|---|
| Status | Completed |
| Study type | OBSERVATIONAL |
| Enrollment | 475 |
| Start date | 23 September 2024 |
| Primary completion | 10 January 2025 |
| Estimated completion | 10 January 2025 |
| Sites | 1 location across China |
Conditions studied
- Difficult Airway — all drugs for Difficult Airway →
Sponsor
Min Su — full company profile →
Who can join
18 and older, any sex, with Difficult Airway. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
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.
Publications & conference data
No peer-reviewed publications indexed yet for this trial. Completed trials usually publish results within 12-18 months.
Verify or expand the search:
- PubMed search for NCT06626204
- Europe PMC full search
- ASCO Meeting Library
- ESMO Meeting Library
- bioRxiv preprints
- medRxiv preprints
- Google Scholar
Related trials
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Currently open trials in the same condition.
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Trials by the same sponsor.
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT06626204 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Min Su
- Last refreshed: 18 February 2026
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT06626204.
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