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

Shanghai Jiao Tong University logo

Shanghai Jiao Tong University

Status

Enrolling

Conditions

Difficult Airways

Treatments

Diagnostic Test: Ultrasonic test

Study type

Observational

Funder types

Other

Identifiers

NCT05207254
SH9H-2021-T356-2

Details and patient eligibility

About

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.

Enrollment

4,000 estimated patients

Sex

All

Ages

18+ years old

Volunteers

No Healthy Volunteers

Inclusion criteria

  • ASA classification is 1-3
  • Patients who intend to undergo tracheal intubation under general anesthesia
  • Age ≥ 18 years old

Exclusion criteria

  • 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 design

4,000 participants in 2 patient groups

difficult airway
Description:
C-L≥3 grade
Treatment:
Diagnostic Test: Ultrasonic test
none difficult airway
Description:
C-L\<3 grade
Treatment:
Diagnostic Test: Ultrasonic test

Trial contacts and locations

1

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Data sourced from clinicaltrials.gov

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