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Research on predicting the risk of intubation difficulty in general anesthesia patients using a deep learning radiomics model based on CT images

Research on predicting the risk of intubation difficulty in general anesthesia patients using a deep learning radiomics model based on CT images

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500102887
Enrollment
Unknown
Registered
2025-05-21
Start date
2025-06-01
Completion date
Unknown
Last updated
2025-05-26

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

Conditions

Difficult airways during general anesthesia

Interventions

Difficult airway group vs Non-Difficult airway group:NA

Sponsors

The First Affiliated Hospital of Zhengzhou University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. Patients planning to undergo elective general anesthesia surgery; 2. Voluntary signing or informed consent provided by family members; 3. Patients who have undergone preoperative head and neck CT scans.

Exclusion criteria

Exclusion criteria: 1. Patients who have received any investigational drug treatment within 30 days prior to the trial or within less than 7 half lives; 2. Known patients with allergies to anesthetic drugs, severe organ dysfunction, and cardiovascular diseases (asthma or recent upper respiratory tract infections); 3. Suffering from mental illnesses such as elevated intracranial pressure and epilepsy. 4. Patients planning to undergo conscious endotracheal intubation. 5. Patients without preoperative CT imaging.

Design outcomes

Primary

MeasureTime frame
Is there any difficulty in the airway;Accuracy ;Sensitivity;

Countries

China

Contacts

Public ContactNa Xing

The First Affiliated Hospital of Zhengzhou University

fccxingn@zzu.edu.cn+86 139 4909 5172

Outcome results

None listed

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