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Construct a deep learning model for predicting difficult laryngoscopy exposure using head and neck Computed Tomography

Construction and validation of a deep learning model for predicting difficult laryngoscopy exposure using head and neck Computed Tomography: A retrospective clinical study

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500108487
Enrollment
Unknown
Registered
2025-09-01
Start date
2025-09-01
Completion date
Unknown
Last updated
2025-09-08

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

Conditions

difficult airway

Interventions

Difficult laryngoscopy:None
Non-difficult laryngoscopy:None

Sponsors

Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1. Aged 18 years old or above; 2.Underwent general anesthesia with endotracheal intubation surgery at the Ninth People's Hospital affiliated with Shanghai Jiao Tong University School of Medicine. 3.Had undergone head and neck CT scans at our hospital within 3 months prior to the surgery, with accessible imaging data.

Exclusion criteria

Exclusion criteria: 1.The patient did not undergo laryngoscopy (e.g., in cases where fiberoptic bronchoscope intubation was selected). 2.No Cormack-Lehane grading was documented for the patient. 3.Excessive missing baseline data (missing values >= 20%). 4.The patient's CT images failed to meet the requirements for model construction.

Design outcomes

Primary

MeasureTime frame
Cormack-Lehane Grading;—

Countries

China

Contacts

Public ContactJiayi Wang

Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine

iriswjy1991@gamil.com+86 136 2164 5800

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026