An observational study in Dental Anxiety in Children, sponsored by Marmara University. Completed at 1 site in Turkey (Türkiye). Open to participants aged 8 Years to 12 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-09-02.
Sponsored by Marmara University · Observational
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.
Dental anxiety can adversely affect a child's cooperation, treatment experience, and willingness to attend future dental appointments. Existing assessment methods primarily rely on self-report questionnaires, visual scales, and clinical observation. Artificial intelligence-based analysis of speech may provide an additional objective and non-invasive method for recognizing dental anxiety before treatment.
This prospective, cross-sectional observational study included children aged 8-12 years attending the Department of Pediatric Dentistry at Marmara University Faculty of Dentistry between September 2025 and February 2026. Data were collected within the natural workflow of an active pediatric dentistry clinic. The study did not assign participants to any treatment or alter their planned dental care.
Before dental treatment, each participant took part in a standardized 1-3-minute conversation conducted by the same researcher. The questions addressed everyday life, school, oral hygiene habits, previous dental experiences, and expectations regarding the planned dental visit. Speech was recorded using a standardized microphone position and recording protocol.
Immediately after the speech recording and before treatment, dental anxiety was assessed using the Children's Fear Survey Schedule-Dental Subscale, the Modified Child Dental Anxiety Scale, and the Face Image Scale. Scores from each measure were categorized into lower and higher anxiety levels using established thresholds and served as reference labels for machine learning model development.
All data were anonymized before analysis. The researchers' speech and prolonged silent segments were removed from the recordings, and the audio files were converted to a standardized format. Acoustic characteristics were extracted from the recordings using OpenSMILE. The spoken responses were transcribed and analyzed using natural language processing methods based on Whisper and a Turkish-language BERT model. Selected acoustic and linguistic features were combined into a multimodal dataset.
Random Forest, XGBoost, LightGBM, and CatBoost algorithms were trained and evaluated using five-fold cross-validation. Model performance was assessed using accuracy, sensitivity, specificity, precision, F1 score, the area under the receiver operating characteristic curve, predictive values, and confusion matrices. Agreement between model predictions was also evaluated. The primary objective was to identify the machine learning approach that most accurately and sensitively distinguished children with lower and higher dental anxiety levels.
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.
IInclusion Criteria:
Exclusion Criteria:
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
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.
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.
Time frame: Day 1, during the single pre-treatment assessment
Sensitivity of the Multimodal Machine Learning Models
Sensitivity was defined as the proportion of participants with higher dental anxiety who were correctly identified by each machine learning model. Sensitivity was calculated separately using the CFSS-DS, MCDAS, and FIS classifications as reference standards. Values range from 0 to 1, with higher values indicating better detection of children with higher dental anxiety.
Time frame: Day 1, during the single pre-treatment assessment
F1 Score of the Multimodal Machine Learning Models
The F1 score was calculated as the harmonic mean of precision and sensitivity to evaluate the balance between correctly identifying children with higher dental anxiety and limiting incorrect classifications. Macro and weighted F1 scores were calculated for each machine learning model using CFSS-DS, MCDAS, and FIS classifications as reference standards. Values range from 0 to 1, with higher values indicating better performance.
Time frame: Day 1, during the single pre-treatment assessment
Plan to share: No
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Marmara University