1. What data in particular will be shared?
   Response - All of the individual participant data collected during the trial, after de-identification.
   


2. What additional supporting information will be shared?
   Response -  Study Protocol
   Response -  Statistical Analysis Plan
   Response - Informed Consent Form
   Response - Clinical Study Report
   
   
3. Who will be able to view these files?
   Response - Researchers whose proposed use of the data has been approved by an independent review committee identified for this purpose.
   


4. For what types of analyses will this data be available?
   Response - To achieve aims in the approved proposal.
   


5. By what mechanism will data be made available?
   Response - Proposals should be directed to [dr.sgmehandale@gmail.com].
   


6. For how long will this data be available start date provided 01-01-2022 and end date provided 01-01-2027?
   Response - Beginning 3 months and ending 5 years following article publication.
   


7. Any URL or additional information regarding plan/policy for sharing IPD? 
   Additional Information - NIL

Airway management poses challenges of oxygenation cannot be maintained by either face mask ventilation, using a supraglottic device, endotracheal intubation or surgical airway (e.g., tracheostomy), when patient looses consciousness or breathing is compromised due to any reason. however, there is no single predictive test to anticipate such difficulties with maximum certainity as the difficult airway is multi factorial. If a system can be developed using artificial intelligence, that is machine learning using image processing, such situations can be instantly diagnosed, anticipated, managed correctly without any damages to the patient.

Hypothesis: Incorporating the airway assessment tools with logic into an app that can accurately predict difficult airway in unanticipated cases will help in accurately predicting and managing difficult airway.

AIM

To develop automated system using machine learning to predict the patients with difficulty in airway management.

 OBJECTIVES

Primary objective:

To design an automated system using machine learning algorithms to predict the difficulty in managing airway.

Secondary objectives:

To predict difiiculty in mask ventilation

To predict difficulty in supraglottic airway insertion

To Predict difficulty in laryngoscopy

To predict difficulty in endotracheal intubation

Compare them with actual clinical outcome

Here we are intending to recruit 250 patients undergoing elective surgeries. Record their airway parameters, take frontal and profile pictures of head and neck, record the outcomes of airway management (mask ventilation, laryngoscopy, use of supraglottic airway, endotracheal intubation etc.) then compare the outcome with the airway indices, form an algorithm to predict difficulty in managing airway, process the images to arrive at predictors of difficulty in airway management. Thus, finally using machine learning launch an app which can predict difficulty in any of the above said parameters, soon after taking photographs in above said views with clinically useful accuracy.