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Airway assessment model based on multimodal and multi-task machine learning and its clinical feasibility study

An observational study of an airway assessment model based on multimodal and multi-task machine learning and its clinical feasibility

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500103756
Enrollment
Unknown
Registered
2025-06-05
Start date
2025-06-09
Completion date
Unknown
Last updated
2025-09-15

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

Conditions

Difficult airway

Interventions

Case series:None

Sponsors

Beijing Anzhen Hospital Affiliated to Capital Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 70 Years

Inclusion criteria

Inclusion criteria: 1. Patients aged 18-70 years; 2. Patients requiring tracheal intubation under general anaesthesia; 3. ASA classification of I-III.

Exclusion criteria

Exclusion criteria: 1. Patients with mental disorders; 2. Patients have difficulty communicating, making it impossible to follow the doctor's instructions.

Design outcomes

Primary

MeasureTime frame
accuracy;—

Secondary

MeasureTime frame
sensitivity;Specificity;Area under the ROC curve;—

Countries

China

Contacts

Public ContactBai Yunbo; Wang Sheng

Beijing Anzhen Hospital Affiliated to Capital Medical University

17888810676@163.com+86 178 8881 0676

Outcome results

None listed

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