NCT05207254 is a clinical trial of Ultrasonic test in Difficult Airways, sponsored by Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University.

What drugs are being tested in NCT05207254?

Interventions in NCT05207254: Ultrasonic test.

What condition does NCT05207254 study?

NCT05207254 studies Difficult Airways.

Is NCT05207254 still recruiting?

NCT05207254 is currently status unknown. Last updated 13 July 2022.

Last reviewed 2022-07-13 · How we verify

NCT05207254

Name: Drug Landscape Pharmaceutical Database
Creator: Drug Landscape
Published: 2026-01-01
License: https://creativecommons.org/licenses/by/4.0/

Research on the Application of Artificial Intelligence Ultrasonic Recognition Technology in Difficult Airway Assessment

Status unknown Last updated 13 July 2022

What this trial tests

trial testing Ultrasonic test in Difficult Airways in 4,000 participants. Status unknown.

Timeline

18 December 2021

Primary endpoint
1 December 2025

30 December 2025

Quick facts

Lead sponsor	Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University
Status	Status unknown
Study type	OBSERVATIONAL
Enrollment	4,000
Start date	18 December 2021
Primary completion	1 December 2025
Estimated completion	30 December 2025
Sites	1 location across China

Drugs / interventions tested

Ultrasonic test

Conditions studied

Difficult Airways — all drugs for Difficult Airways →

Sponsor

Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University

Who can join

18 and older, any sex, with Difficult Airways. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

Although there is no related research on the evaluation of difficult airways by ultrasound features based on artificial intelligence, the investigators guess that the evaluation of ultrasound features based on artificial intelligence can make further breakthroughs in difficult airway early warning systems. Therefore, this project intends to use AI technology to extract and analyze the ultrasound features of the subjects, evaluate the correlation between the ultrasound features of the subjects and the occurrence of difficult airways, and construct possible diagnostic models to evaluate AI ultrasound feature recognition in the prediction of difficult airways. The effect and application value of this method are expected to be more intelligent and accurate for early warning of difficult airways in clinical anesthesia.

Publications & conference data

1 peer-reviewed publication reference this trial (live from Europe PMC):

A two-step deep learning framework for predicting difficult video laryngoscopy from ultrasound images: a prospective cohort study.
Jin C, Pei B, Zhou R, Cao S, et al · · 2026 · PMID 42226131 · DOI 10.1186/s12871-026-03948-z

Verify or expand the search:

Other Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University trials

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)

Data sources for this page

Trial protocol + status: ClinicalTrials.gov NCT05207254 (US National Library of Medicine, public domain)
Publications: Europe PMC API search by NCT ID, retrieved 10 June 2026
Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
Sponsor: as reported to ClinicalTrials.gov by Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University
Last refreshed: 13 July 2022

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/NCT05207254.

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