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A Cohort Study on Constructing a Machine Learning Model for Predicting Difficult Airway by Multimodal Dynamic Evaluation of Spatial Position Changes in Peri-airway Anatomical Structures

A Cohort Study on Constructing a Machine Learning Model for Predicting Difficult Airway by Multimodal Dynamic Evaluation of Spatial Position Changes in Peri-airway Anatomical Structures

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600128535
Enrollment
Unknown
Registered
2026-07-22
Start date
2026-08-01
Completion date
Unknown
Last updated
2026-08-03

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Difficult airway

Interventions

Building prediction model patients:None
External validation patients:None

Sponsors

Peking University Third Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 70 Years

Inclusion criteria

Inclusion criteria: 1.Age between 18 and 80 years; 2.American Society of Anesthesiologists (ASA) physical status classification I to III; 3.Patients scheduled to undergo elective anterior cervical decompression and fusion (ACDF) or posterior cervical laminoplasty under general anesthesia with tracheal intubation;

Exclusion criteria

Exclusion criteria: 1.Patients with cervical spine instability, cervical tumors, or thyroid enlargement; 2.Patients anticipated to be difficult airway; 3.Patients unable to cooperate; 4.Patients who declined to participate;

Design outcomes

Primary

MeasureTime frame
Difficult Laryngoscopy Grading;

Secondary

MeasureTime frame
Tracheal Tube Curvature and Spatial Distance Assessment via Orthopedic Fluoroscopic Imaging During Surgery;

Countries

China

Contacts

Public ContactXu Mao

Peking University Third Hospital

anae@163.com+86 10 82265176

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Aug 10, 2026