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"Mobile phone-based test using facial imaging, voice analysis, and ultrasound to predict difficult airway in adults undergoing anesthesia"

Development of a Tri-Modal Machine Learning Model for Predicting Difficult Airway Using Facial Photography, Acoustic Voice Analysis, and Point-of-Care Airway Ultrasound - Nil

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2026/03/105275
Enrollment
100
Registered
2026-03-03
Start date
Unknown
Completion date
Unknown
Last updated
2026-03-30

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

Intervention1: Nil: Nil

Sponsors

Alok G Belgaumkar
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: 1.Age 18 years 2.Scheduled for elective surgical procedures requiring general anesthesia with tracheal intubation 3.ASA physical status I-III 4.Ability to provide informed written consent

Exclusion criteria

Exclusion criteria: 1. Active airway obstruction or emergency airway situations 2. Acute facial trauma or facial disfigurement 3. Vocal cord paralysis or laryngeal pathology 4. Contraindication to ultrasound examination 5. Refusal to participate

Design outcomes

Primary

MeasureTime frame
The tri-modal model will demonstrate significantly higher AUC and improved sensitivity-specificity trade-off compared to traditional scores.Timepoint: At pre-anaesthetic evaluation We will check for the trimodal airway predictors. Then Immediate post-intubation we will confirm the difficult airway—

Secondary

MeasureTime frame
NILTimepoint: NIL—

Countries

India

Contacts

Public ContactAlok Belgaumkar

Sri Sidhartha Academy Of Higher Education, Tumkur

alokbelgaumkar@gmail.com08762771374

Outcome results

None listed

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