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Designing an Automated System to Predict the Cases with Difficulties in Securing the Airway Using Machine Learning Algorithms and Image Processing

Prediction of difficult airway management cases using machine learning and image processing techniques.

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2021/05/033323
Enrollment
250
Registered
2021-05-03
Start date
Unknown
Completion date
Unknown
Last updated
2021-11-24

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

None listed

Sponsors

Dr Sripada Mehandale
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: Adult patients undergoing general anaesthesia for elective surgical procedure

Exclusion criteria

Exclusion criteria:

Design outcomes

Primary

MeasureTime frame
Developing automated system using machine learning to predict the patients with difficulty in airway management.Timepoint: Six months—

Secondary

MeasureTime frame
To predict difiiculty in mask ventilation To predict difficulty in supraglottic airway insertion To Predict difficulty in laryngoscopy To predict difficulty in endotracheal intubation Timepoint: Six months—

Countries

India

Contacts

Public ContactSRIPADA G MEHENDALE

KSHEMA Nitte University

sripadamehandale@nitte.edu.in9448384310

Outcome results

None listed

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