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AI-Assisted Evaluation of Dental Anxiety in Children: A Machine Learning Approach
Sponsor: Marmara University
Summary
This observational study evaluated whether children's dental anxiety could be identified from their speech using artificial intelligence and machine learning methods. Children aged 8-12 years attending a pediatric dentistry clinic answered a set of short, standardized questions before receiving dental treatment. Their speech was recorded, and their dental anxiety was assessed during the same session using three established measures: the Children's Fear Survey Schedule-Dental Subscale, the Modified Child Dental Anxiety Scale, and the Face Image Scale. Acoustic characteristics of the children's voices and linguistic characteristics of their spoken responses were analyzed together. Four machine learning algorithms were developed and evaluated to determine how accurately they could distinguish between children with lower and higher levels of dental anxiety. No treatment was assigned or modified as part of the study.
Key Details
Gender
All
Age Range
8 Years - 12 Years
Study Type
OBSERVATIONAL
Enrollment
262
Start Date
2025-09-10
Completion Date
2026-04-10
Last Updated
2026-09-02
Healthy Volunteers
Yes
Conditions
Interventions
Multimodal Speech-Based Dental Anxiety Assessment
Participants completed a standardized 1-3-minute speech recording before dental treatment. Acoustic and linguistic characteristics of their speech were analyzed using artificial intelligence methods. During the same session, dental anxiety was assessed using the Children's Fear Survey Schedule-Dental Subscale, the Modified Child Dental Anxiety Scale, and the Face Image Scale. The assessment was conducted for research purposes and did not alter the participants' planned dental care.
Locations (1)
Marmara University Faculty of Dentistry
Istanbul, Maltepe, Turkey (Türkiye)