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A study on comparing the accuracy of conventional predictors vs artificial intelligence in predicting difficult intubation in anaesthesia

A study on comparing the accuracy of conventional predictor model versus artificial intelligence in predicting difficult intubation in anaesthesia - Nil

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2023/11/059976
Enrollment
793
Registered
2023-11-17
Start date
Unknown
Completion date
Unknown
Last updated
2023-12-19

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

Control Intervention1: Nil: Nil

Sponsors

Shahana Muneer
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: ASA 1,2 and 3

Exclusion criteria

Exclusion criteria: 1. Developmental anomalies which may affect airway assessment 2. Patients with airway malformations, midline neck swellings, face trauma or other gross external head and neck deformities 3. Psychiatric patients who are unable to follow commands

Design outcomes

Primary

MeasureTime frame
to assess the accuracy of conventional predictor model and artificial intelligence in predicting difficult airwayTimepoint: 18 months—

Secondary

MeasureTime frame
To compare conventional model & artificial intelligence in prediction of difficult intubationTimepoint: 18 months—

Countries

India

Contacts

Public ContactShahana Muneer

Amala institute of medical sciences

mithunraju@hotmail.com9961738780

Outcome results

None listed

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