AI-Assisted Evaluation of Dental Anxiety in Children: A Machine Learning Approach

NCT07797777 | Completed

• 1 other identifier: MUPEDDENGA
• Study Type: observational
• Target: 262
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Dental Anxiety in Children

Keywords

Artificial Intelligence; Machine Learning; Speech Analysis; Speech Emotion Recognition; Natural Language Processing; Pediatric Dentistry; Dental Fear; Multimodal Analysis

Outcome Measures

Primary Outcomes (1)

• Classification Accuracy of the Multimodal Machine Learning Models

  Accuracy was defined as the proportion of participants correctly classified as having lower or higher dental anxiety. Random Forest, XGBoost, LightGBM, and CatBoost models were evaluated separately using reference classifications derived from the CFSS-DS, MCDAS, and FIS. Performance was assessed using five-fold cross-validation. Accuracy values range from 0 to 1, with higher values indicating better classification performance.

  Day 1, during the single pre-treatment assessment

Secondary Outcomes (1)

• Sensitivity of the Multimodal Machine Learning Models

  Day 1, during the single pre-treatment assessment

Other Outcomes (1)

• F1 Score of the Multimodal Machine Learning Models

  Day 1, during the single pre-treatment assessment

Study Arms (1)

Pediatric Dental Patients

Children aged 8-12 years attending a pediatric dentistry clinic who underwent a standardized pre-treatment speech recording and dental anxiety assessment during a single study visit. No dental treatment was assigned, changed, or delayed as part of the study.

Diagnostic Test: Multimodal Speech-Based Dental Anxiety Assessment

Interventions

Multimodal Speech-Based Dental Anxiety Assessment DIAGNOSTIC_TEST

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.

Pediatric Dental Patients

Eligibility Criteria

• Age: 8 Years - 12 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17)
• Sampling Method: Non-Probability Sample

Study Population

he study population consisted of children aged 8-12 years who attended the Department of Pediatric Dentistry at Marmara University Faculty of Dentistry for routine dental care between September 2025 and February 2026. Eligible children completed a standardized speech recording and three dental anxiety assessments during a single visit before dental treatment. Children with both lower and higher levels of dental anxiety were included. Participation did not alter or delay the dental care planned for any child.

You may qualify if:

• Children aged 8-12 years
• Attendance at the Department of Pediatric Dentistry, Marmara University -Faculty of Dentistry
• Native Turkish speaker
• Age-appropriate neuropsychological development
• Ability to communicate, cooperate in the clinical setting, and understand and follow the study instructions
• No chronic or systemic condition affecting speech production, respiratory function, or cognitive processes
• Written informed consent provided by a parent or legal guardian

You may not qualify if:

• Neurological, hearing, speech, or language disorder that could affect clinical communication or study assessments
• Suspected or diagnosed neurodevelopmental disorder, including autism spectrum disorder or attention-deficit/hyperactivity disorder
• Native language other than Turkish
• Active pathology or history of surgery that could affect voice or speech quality, including resonance or phonation
• Inability to provide sufficient verbal data, such as consistently giving single-word responses or leaving multiple questions unanswered

Sponsors & Collaborators

• Marmara University: lead

Study Sites (1)

Marmara University Faculty of Dentistry

Istanbul, Maltepe, 34854, Turkey (Türkiye)

Location

Study Officials

• Gizem Akova

  Marmara University Faculty Of Dentistry

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: August 27, 2026
• First Posted: September 1, 2026
• Study Start: September 10, 2025
• Primary Completion: February 10, 2026
• Study Completion: April 10, 2026
• Last Updated: September 2, 2026

Record last verified: 2026-09

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)