Dental Anxiety in ChildrenNCT07797777
AI-Assisted Evaluation of Dental Anxiety in Children: A Machine Learning Approach
An observational study of Dental Anxiety in Children, run by Marmara University at 1 site in Turkey (Türkiye).
Trial 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. Read more about this study
- Who is it for?
- Ages 8 to 12 yearsHealthy volunteers can take part
- Conditions the study is for
- Dental Anxiety in Children
- What is being tested?
- Multimodal Speech-Based Dental Anxiety Assessment
- How many people
- 262 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 10 Sep 2025to 10 Apr 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 8 to 12 years
- All sexes
- Healthy volunteers accepted
- Child
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.
Who may be able to join
None listed.
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site
Marmara University Faculty of Dentistry
Istanbul, Maltepe, 34854
About this study
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.
Read the detailed description
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.
- Conditions
- Dental Anxiety in Children
- Being tested
- Multimodal Speech-Based Dental Anxiety Assessment
- Keywords
- Artificial Intelligence, Machine Learning, Speech Analysis, Speech Emotion Recognition, Natural Language Processing, Pediatric Dentistry, Dental Fear, Multimodal Analysis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Multimodal Speech-Based Dental Anxiety AssessmentDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: Multimodal Speech-Based Dental Anxiety Assessment |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Day 1, during the single pre-treatment assessment
Secondary outcomes (1)
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.
Measured: Day 1, during the single pre-treatment assessment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Sep 2026
- Study start10 Sep 2025
- Primary completion10 Feb 2026
- Study completion10 Apr 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Dental Anxiety in ChildrenStudies of the same condition, open and under way.
- More trials run by Marmara UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Cross-sectional
- Enrolment
- 262 participants (actual)
Sponsor and investigators
- Lead sponsor
- Marmara University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Gizem Akova, Marmara University
- Investigators
- Gizem Akova, Principal investigator, Marmara University Faculty Of Dentistry
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07797777
- Sponsor's protocol ID
- MUPEDDENGA
- Status last verified
- Sep 2026
- First submitted
- 27 Aug 2026
- Last updated
- 2 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 2 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-Assisted Evaluation of Dental Anxiety in Children: A Machine Learning Approach
- ClinicalTrials.gov ID
- NCT07797777
- Acronym / sponsor ID
- MUPEDDENGA
- Conditions
- Dental Anxiety in Children
- Keywords
- Artificial Intelligence, Machine Learning, Speech Analysis, Speech Emotion Recognition, Natural Language Processing, Pediatric Dentistry, Dental Fear, Multimodal Analysis
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 262 participants (actual)
- Lead sponsor
- Marmara UniversityUniversity, hospital or other
- Study start
- 10 Sep 2025 (actual)
- Primary completion
- 10 Feb 2026 (actual)
- Study completion
- 10 Apr 2026 (actual)
- First posted
- 1 Sep 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Observational model
- Case only
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Pediatric Dental Patients | – | Diagnostic Test: Multimodal Speech-Based Dental Anxiety AssessmentChildren 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. |
Who may qualify
- Ages
- 8 to 12 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child
- 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.
Key inclusion criteria (0 listed)
Key exclusion criteria (5 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Multimodal Speech-Based Dental Anxiety AssessmentParticipants 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. | Diagnostic test | Pediatric Dental Patients |
Primary outcome
Classification Accuracy of the Multimodal Machine Learning Models
Time frame: Day 1, during the single pre-treatment assessment
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.
1 secondary outcome
Sensitivity of the Multimodal Machine Learning Models
Time frame: Day 1, during the single pre-treatment assessment
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Sep 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Gizem Akova principal investigator
Marmara University Faculty Of Dentistry
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Dental Anxiety in Children, run by Marmara University at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 0 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 2 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Classification Accuracy of the Multimodal Machine Learning Models (time frame: Day 1, during the single pre-treatment assessment). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Sep 2026.