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Research on Deep Learning Prediction Model for Difficult Airways in Cervical Spine Surgery Based on Medical Images

Research on Deep Learning Prediction Model for Difficult Airways in Cervical Spine Surgery Based on Medical Images

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600118254
Enrollment
Unknown
Registered
2026-02-03
Start date
2026-03-01
Completion date
Unknown
Last updated
2026-02-09

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

Conditions

Cervical Spine surgery

Interventions

Observational group:None

Sponsors

Peking University Third Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 85 Years

Inclusion criteria

Inclusion criteria: 1. Preoperative diagnosis of cervical spondylosis; 2. Patients undergoing elective cervical spine surgery under general anesthesia; 3. Age >= 18 years old; 4. American Society of Anesthesiologists Classification I to III; 5. A cervical spine MRI examination has been performed.

Exclusion criteria

Exclusion criteria: 1. Pregnant woman; 2. Cervical immobilization; 3. Oropharyngeal mass; 4. Those who are determined by researchers to be unable to communicate normally; 5. Refuse to sign the informed consent form.

Design outcomes

Primary

MeasureTime frame
MRI imaging indicators: Labeling of soft tissue structures from the tongue to the larynx (including local muscles and adjacent glands);Cormack-lehane (CL) classification;—

Secondary

MeasureTime frame
Appearance indicators: gen-to-chin distance, modified Mallampati grade, neck circumference/mouth width;—

Countries

China

Contacts

Public ContactWang Mingya

Peking University Third Hospital

wangmingya@bjmu.edu.cn+86 153 2102 7696

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 15, 2026