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Using artificial intelligence to predict factors affecting difficult breathing tube insertion during operation

Prediction of difficult laryngoscopy in adult elective surgery patients using machine learning: a prospective observational study - Nil

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2026/06/112303
Enrollment
1147
Registered
2026-06-09
Start date
Unknown
Completion date
Unknown
Last updated
2026-06-22

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

Conditions

Health Condition 1: O- Medical and Surgical

Interventions

Intervention1: Direct laryngoscopy: Adults elective patients undergoing Surgery Intervention2: Nil: Nil Intervention3: Nil: Nil Control Intervention1: Nil: Nil

Sponsors

King Georges Medical University
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: 1. Patients undergoing elective surgery with endotracheal tube placement using direct laryngoscopy for airway management. 2. American Society Of anesthesiologists Physical status 1-4

Exclusion criteria

Exclusion criteria: 1. Refusal to give consent 2. Restricted mouth opening 3. Pregnancy 4. Known airway pathology 5. Cervical spine instability

Design outcomes

Primary

MeasureTime frame
Incidence of difficult laryngoscopyTimepoint: During intubation of the patient—

Secondary

MeasureTime frame
Incidence of difficult intubationTimepoint: During intubation of the patient—

Countries

India

Contacts

Public ContactDr Rajesh Raman

King George Medical University

ramanrajesh83@gmail.com9451564339

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Jun 29, 2026