Difficult AirwaysNCT05207254
Research on the Application of Artificial Intelligence Ultrasonic Recognition Technology in Difficult Airway Assessment
An observational study of Difficult Airways, run by Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University at 1 site in China.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Difficult Airways
- What is being tested?
- Ultrasonic test
- How many people
- 4,000 participants
- Where
- 1 siteChina
- Study dates
- 18 Dec 2021to 30 Dec 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: adults who intend to undergo tracheal intubation under general anesthesia
Who may be able to join
- ASA classification is 1-3
- Patients who intend to undergo tracheal intubation under general anesthesia
- Age ≥ 18 years old
Who cannot join
- Patients with speech communication and cooperation barriers;
- Patients with open head and neck trauma
- Patients with cervical spine fractures or cervical spine diseases;
- Emergency surgery;
- Patients who are allergic to related drugs.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Shanghai 9Th Hospital
Shanghai, Shanghai Municipality, 200000
Hong Jiang, +8615121007303, jianghongjiuyuan@163.com
About this study
Official title: Research on the Application of Artificial Intelligence Ultrasonic Recognition
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.
- Conditions
- Difficult Airways
- Being tested
- Ultrasonic test
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Ultrasonic testDiagnostic test
Ultrasonic test to the patient's head, neck and jaw area.
| Study group | Role | What they receive |
|---|---|---|
| difficult airway | C-L≥3 grade Receives: Diagnostic Test: Ultrasonic test | |
| none difficult airway | C-L<3 grade Receives: Diagnostic Test: Ultrasonic test |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnosed as a difficult airway
Cormack-Lehane was used for grading the best glottic view. In grade the entire glottis was visible; in grade 2 a portion of the glottis was visible; in grade 3 only the epiglottis could be seen; and in grade 4 the epiglottis was not visible. A score of 3-4 indicated difficult video laryngoscopy
Measured: 0-10minutes after intubation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Jan 2022
- Study start18 Dec 2021
- Primary completion1 Dec 2025 (estimated)
- Study completion30 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Difficult AirwaysStudies of the same condition, open and under way.
- More trials run by Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 4,000 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05207254
- Sponsor's protocol ID
- SH9H-2021-T356-2
- Status last verified
- Dec 2021
- First submitted
- 23 Dec 2021
- Last updated
- 13 Jul 2022
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Jin C, Pei B, Zhou R, Cao S, Sun Y, Lin Z, Zhao M, Zheng Y, Wang Q, Cao M, Yan J, Jiang H. A two-step deep learning framework for predicting difficult video laryngoscopy from ultrasound images: a prospective cohort study. BMC Anesthesiol. 2026 Jun 1;26(1):464. doi: 10.1186/s12871-026-03948-z.
PubMed 42226131
Study at a glance
From the registry record, last updated by the sponsor on 13 Jul 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research on the Application of Artificial Intelligence Ultrasonic Recognition
- ClinicalTrials.gov ID
- NCT05207254
- Acronym / sponsor ID
- SH9H-2021-T356-2
- Conditions
- Difficult Airways
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 4,000 participants (estimated)
- Lead sponsor
- Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong UniversityUniversity, hospital or other
- Study start
- 18 Dec 2021 (actual)
- Primary completion
- 1 Dec 2025 (estimated)
- Study completion
- 30 Dec 2025 (estimated)
- First posted
- 26 Jan 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| difficult airway | – | Diagnostic Test: Ultrasonic testC-L≥3 grade |
| none difficult airway | – | Diagnostic Test: Ultrasonic testC-L<3 grade |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- adults who intend to undergo tracheal intubation under general anesthesia
Key inclusion criteria (3 listed)
- ASA classification is 1-3
- Patients who intend to undergo tracheal intubation under general anesthesia
- Age ≥ 18 years old
Key exclusion criteria (5 listed)
- Patients with speech communication and cooperation barriers;
- Patients with open head and neck trauma
- Patients with cervical spine fractures or cervical spine diseases;
- Emergency surgery;
- Patients who are allergic to related drugs.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Ultrasonic testUltrasonic test to the patient's head, neck and jaw area. | Diagnostic test | difficult airway, none difficult airway |
Primary outcome
Diagnosed as a difficult airway
Time frame: 0-10minutes after intubation
Cormack-Lehane was used for grading the best glottic view. In grade the entire glottis was visible; in grade 2 a portion of the glottis was visible; in grade 3 only the epiglottis could be seen; and in grade 4 the epiglottis was not visible. A score of 3-4 indicated difficult video laryngoscopy
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Jul 2022.
- Derived from this study: PubMed 42226131Jin C, Pei B, Zhou R, Cao S, Sun Y, Lin Z, Zhao M, Zheng Y, Wang Q, Cao M, Yan J, Jiang H. A two-step deep learning framework for predicting difficult video laryngoscopy from ultrasound images: a prospective cohort study. BMC Anesthesiol. 2026 Jun 1;26(1):464. doi: 10.1186/s12871-026-03948-z.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Difficult Airways, run by Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 13 Jul 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnosed as a difficult airway (time frame: 0-10minutes after intubation). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05207254, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Jul 2022.