Determining whether Deep Learning Analysis of Facial Imaging is Effective in Predicting Difficult Intubatio

Not Applicable

• Conditions: Difficult intubation; Airway management; Anaesthesiology - Anaesthetics

• Registration Number: ACTRN12621001020875
• Lead Sponsor: Dr Jonathon Stewart

Brief Summary

Not available

Detailed Description

Not available

Study Timeline

• Results First Posted: 1900-01-01
• Study Start: 2021-08-04
• Last Update Posted: 10 August 2021

Recruitment & Eligibility

• Status: ot yet recruiting
• Sex: All
• Target Recruitment: 250

Inclusion Criteria

Adult patients undergoing elective surgery that are anticipated to require intubation.

Exclusion Criteria

Patients will be excluded if after enrollment they do not undergo intubation at surgery, their surgery is cancelled, or their data collection form is not completed by the treating anaesthetist.

Study & Design

• Study Type: Observational
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Deep learning model accuracy in classifying patients level of intubation difficulty. Accuracy will be assessed by comparing the number of difficult intubations identified by the deep learning model to number of difficulty intubations identified by the anaesthetist (prior to actual intubation). [Photographs to be used as input for deep learning model will be determined at baseline. <br>Surgery up to 6 months after end of patient recruitment]

Secondary Outcome Measures

Name	Time	Method
il[Nil]