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To develop and evaluate an AI-based facial emotion recognition application for dental anxiety detection during extraction procedures in children aged 7 to 13 years

Artificial intelligence-based facial emotion recognition for real-time assessment of paediatric dental anxiety - NIL

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2026/04/108541
Enrollment
211
Registered
2026-04-16
Start date
Unknown
Completion date
Unknown
Last updated
2026-06-01

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Health Condition 1: K029- Dental caries, unspecified

Interventions

Intervention1: Nil: Nil

Sponsors

University College of Medical Sciences
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: • Medically healthy children, ASA one or two with no psychiatric or neurological impairments. • Aged seven to thirteen years, suitable for emotional response and dental anxiety assessment. • Presenting with grossly decayed teeth requiring extraction under local anesthesia. • Written informed consent from parent/guardian and child assent, as appropriate. • Proficiency in a suitable language (example Hindi) for effective communication. • Willingness to be video capture in real time during the dental tooth extraction for facial emotion analysis.

Exclusion criteria

Exclusion criteria: • Children with known history of developmental delays, autism spectrum disorder, or other neurological or psychiatric conditions affecting facial expressions or behavior. • History of known facial trauma, surgery, or congenital anomalies affecting the face • Children undergoing procedures with sedation, general anesthesia, or pre-medication that may alter emotional expression or behavior. • Children attending emergency or urgent dental care, where anxiety levels may be atypically high and not representative of routine visits. • Uncooperative behavior preventing adequate video capture of facial expressions.

Design outcomes

Primary

MeasureTime frame
1.To design and develop an open-source Swift-based app capable of real-time facial emotion detection and classification using a pre-trained machine learning model. 2.To assess the clinical applicability, enabling real-time usability of tool during treatment in pediatric dental settings. Timepoint: five seconds after local anaesthesia

Secondary

MeasureTime frame
To validate the systemâ??s performance by comparing detected emotional states with traditional behavioural assessment tools (Venham Clinical Anxiety Scale & Visual Facial Anxiety Scale).Timepoint: five seconds after local anaesthesia

Countries

India

Contacts

Public ContactDr Padma Yangdol

University College of Medical Sciences

padmaj2718@gmail.com8860865850

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Jun 11, 2026