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

Not yet recruiting

• Conditions: Medical and Surgical,

• Registration Number: CTRI/2023/11/059976
• Lead Sponsor: Shahana Muneer

Brief Summary

This study aims to assess the accuracy of conventional predictor model of difficult airway prediction (including parameters like BMI , neck circumference, thyromental distance, interincisor gap, mallampatti classification, age  and head and neck movements) and artificial intelligence model using random forest classifier. Using a Macintosh blade of size 3 or 4 , laryngoscopy will be done and vocal cord is graded according to cormack lehane grading. Grades 1 and 2 are considered as easy and 3 and 4 as difficult airways.

Detailed Description

Not available

Study Timeline

• Last Update Posted: 2023-09-11
• Study Start: 2023-11-21

Recruitment & Eligibility

• Status: Not Yet Recruiting
• Sex: All
• Target Recruitment: 793

Inclusion Criteria

ASA 1,2 and 3.

Exclusion Criteria

• 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.

Study & Design

• Study Type: Observational
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
to assess the accuracy of conventional predictor model and artificial intelligence in predicting difficult airway	18 months

Secondary Outcome Measures

Name	Time	Method
To compare conventional model & artificial intelligence in prediction of difficult intubation	18 months

Trial Locations

Locations (1)

Amala Institute of Medical Sciences; 🇮🇳 Thrissur, KERALA, India

Amala Institute of Medical Sciences

🇮🇳Thrissur, KERALA, India

Dr Shahana Muneer

Principal investigator

9562934551

kukku.j@yahoo.com