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Clinical Trials/CTRI/2023/11/059976
CTRI/2023/11/059976Not yet recruitingNot Applicable

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

Shahana Muneer1 site in 1 country793 target enrollmentStarted: November 21, 2023Last updated:
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Trial Snapshot

Phase
Not Applicable
Status
Not yet recruiting
Sponsor
Enrollment
793
Locations
1
Primary Endpoint
to assess the accuracy of conventional predictor model and artificial intelligence in predicting difficult airway
Study OverviewStudy DesignEligibility CriteriaOutcomesInvestigatorsSitesRecords & IdentifiersSimilar Trials

Study Overview

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.

Study Design

Study Type
Observational

Eligibility Criteria

Ages
18.00 Year(s) to 80.00 Year(s) (—)
Sex
All

Inclusion Criteria

  • •ASA 1,2 and 3.

Exclusion Criteria

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

Outcomes

Primary Outcomes

to assess the accuracy of conventional predictor model and artificial intelligence in predicting difficult airway

Time Frame: 18 months

Secondary Outcomes

  • To compare conventional model & artificial intelligence in prediction of difficult intubation(18 months)

Investigators

Sponsor
Shahana Muneer
Sponsor Class
Other [self]

Study Sites (1)

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