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A Comparative Study of Machine-Learning Tools for Difficult Airway Prediction in anesthesia

A study on comparative evaluation of machine learning algorithms for predicting difficult airways - NIL

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2025/12/098666
Enrollment
697
Registered
2025-12-08
Start date
Unknown
Completion date
Unknown
Last updated
2026-01-12

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: Nil: Nil

Sponsors

Shahana Muneer
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: 1. Surgical procedures requiring endotracheal intubation using the Macintosh blade 2. Age : 18-80years 3. ASA I,II, III

Exclusion criteria

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

Design outcomes

Primary

MeasureTime frame
1.To compare the predictive performance of various machine learning algorithms in identifying difficult airway cases.Timepoint: 3months—

Secondary

MeasureTime frame
2. To determine the most clinically useful machine learning model for potential integration into preoperative airway assessment workflows.Timepoint: 3 months;3. To identify the optimal subset or combination of predictors that yields the highest predictive accuracy for each machine learning algorithm.Timepoint: 3 months—

Countries

India

Contacts

Public ContactShahana Muneer

Amala institute of medical sciences

kukku.j@yahoo.com09562934551

Outcome results

None listed

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