An observational study in Dental Caries, Caries Risk and Dental Plaque, sponsored by Marmara University. Not yet recruiting at 1 site in Turkey (Türkiye). Open to participants aged 4 Years to 13 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-11-10.
Sponsored by Marmara University · Observational
This study aims to develop and evaluate an artificial intelligence (AI)-based system that can detect dental caries and dental plaque in children using intraoral digital scans and photographs, and use these findings to estimate each child's risk of developing dental caries.
Children aged 4-13 years who attend Marmara University Faculty of Dentistry, Department of Pediatric Dentistry for routine dental care will be invited to participate. Participants will receive a clinical dental examination. Dental caries will be evaluated using standard clinical criteria, and dental plaque will be assessed using a plaque index. Intraoral photographs and three-dimensional digital scans of the teeth will also be obtained. Parents will complete a questionnaire about their child's toothbrushing habits, sugar consumption, fluoride use, dental visits, and selected sociodemographic factors.
The collected digital images and scans will be used to train and test an AI model designed to automatically identify and measure areas of dental caries and plaque. The AI findings will then be combined with clinical examination results and oral health-related information to classify children as having low, moderate, or high caries risk.
Based on the assigned caries risk level, individualized preventive recommendations such as oral hygiene instruction, dietary counseling, fluoride applications, professional plaque control, and follow-up visits will be provided. Participants will be followed according to their risk level and digital scans and clinical assessments will be repeated to evaluate changes in caries, plaque, and caries risk over time.
The study is expected to provide information on whether AI-assisted digital imaging can support more objective, standardized and personalized caries risk assessment and preventive dental care in children.
Dental caries is a multifactorial disease, and accurate assessment of both current disease status and future caries risk is important for planning individualized preventive care. Conventional caries risk assessment relies on the combined evaluation of clinical findings, oral hygiene, dietary habits, fluoride exposure, previous caries experience, and other risk or protective factors. However, this process may vary between clinicians and may be influenced by subjective interpretation. The present study is designed to investigate whether artificial intelligence (AI)-assisted analysis of digital dental images can contribute to a more objective, standardized, and reproducible approach to caries and dental plaque assessment in children.
Clinical dental examinations will be performed using standardized criteria. Dental caries will be recorded according to ICDAS II criteria, caries experience will be documented using DMFT/dft indices, and dental plaque will be assessed using a standardized plaque index. In addition, standardized intraoral photographs and three-dimensional intraoral digital scans will be obtained. Information related to oral health behaviors, including toothbrushing frequency, sugar consumption, fluoride exposure, and dental attendance, will be collected through a structured parental questionnaire.
The digital data will be used to develop an AI-based image analysis system for the automatic detection and segmentation of dental caries and plaque. An instance-segmentation approach is planned so that both localized carious lesions and irregularly distributed plaque areas can be identified at the pixel level rather than only by bounding boxes. The model will be developed using transfer learning from pretrained weights and will be retrained using the intraoral image and scan dataset generated within the study.
Reference annotations for model development will be created manually according to predefined clinical criteria. The annotated dataset will be divided into training, validation, and test subsets. Model performance will be evaluated using detection and segmentation metrics such as precision, recall, F1-score, mean average precision, Dice coefficient, and Intersection over Union. Additional validation procedures, including cross-validation and comparison of different convolutional approaches, are planned to improve model robustness and generalizability. Visualization methods such as Grad-CAM will also be used to improve the clinical interpretability of model decisions.
After model training and testing, the optimized system will be integrated into a clinical decision-support interface. For newly evaluated participants, intraoral scan data, with photographs when necessary, will be analyzed automatically. The system will provide outputs including the number, location, and severity of detected carious lesions and the estimated extent of dental plaque. These AI-derived findings will be combined with clinical indices and behavioral information to generate an individualized caries risk assessment.
Participants will be classified into predefined caries-risk categories using a combination of clinical disease indicators, plaque findings, AI-derived lesion and plaque measurements, and behavioral risk and protective factors. The resulting risk classification will be used to support individualized preventive recommendations. Repeated clinical and digital evaluations will allow changes in caries findings, plaque levels, and risk status to be assessed over time.
The study is intended to determine whether combining AI-based analysis of intraoral digital data with established clinical and behavioral indicators can improve the consistency and clinical usefulness of caries risk assessment in pediatric dentistry. The proposed system is intended as a clinical decision-support tool rather than a replacement for professional dental examination and judgment.
1,171 studies on the registry are indexed under Dental Caries; 209 are open to participants now.
This study's planned enrollment of 250 is above the median of 174 across 187 observational studies indexed under Dental Caries.
Browse Dental Caries studies →Marmara University is the lead sponsor of 599 studies on the registry; 138 are open to participants now.
Counted across the registry records on this site, refreshed daily.
The study population will consist of children aged 4-13 years attending the Department of Pediatric Dentistry at Marmara University Faculty of Dentistry for routine dental care and meeting the study eligibility criteria.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Children classified as low caries risk, with no disease indicators, no or minimal risk factors, and predominant protective factors. This group includes children with no AI-detected lesion, plaque percentage \<10% or plaque index 0, twice-daily brushing with fluoride toothpaste, low sugar consumption, and regular dental attendance. Preventive recommendations will include recall every 6 months, brushing twice daily with fluoride toothpaste, dietary counseling, and fissure sealants when indicated.
Behavioral: individualized preventive recommendations
Children without disease indicators but with at least one risk factor, including a single AI-detected non-cavitated lesion, plaque percentage of 10-29% or plaque index 1-2, increased daily sugar consumption, brushing less than twice daily, irregular dental attendance, or low fluoride exposure. Preventive recommendations will include recall every 3-6 months, oral hygiene instruction, fluoride support when needed, brushing twice daily with fluoride toothpaste, professional fluoride varnish every 6 months, dietary counseling, and fissure sealants.
Behavioral: individualized preventive recommendations
Children classified as high caries risk based on the presence of at least one disease indicator or increased disease burden, including ICDAS ≥3, DMFT/dft \>0, restoration or tooth loss due to caries within the previous 12 months, ≥2 AI-detected lesions, AI-detected lesion with plaque percentage ≥30%, or plaque index ≥2. Preventive recommendations will include recall every 1-3 months, professional plaque control, recommendation of a remineralizing agent (Tooth Mousse or R.O.C.S. Medical Minerals gel), oral hygiene instruction, brushing twice daily with fluoride toothpaste, professional fluoride varnish every 3 months, dietary counseling, and fissure sealants.
Behavioral: individualized preventive recommendations
Participants will be classified into predefined caries-risk categories using a combination of clinical disease indicators, plaque findings, AI-derived lesion and plaque measurements, and behavioral risk and protective factors. Based on the assigned caries risk level, individualized preventive recommendations such as oral hygiene instruction, dietary counseling, fluoride applications, professional plaque control, and follow-up visits will be provided. Participants will be followed according to their risk level, and digital scans and clinical assessments will be repeated to evaluate changes in caries, plaque, and caries risk over time.
Accuracy of the Artificial Intelligence Model for Dental Caries Detection
Accuracy will be calculated as the proportion of correct predictions among all evaluated predictions, using expert-annotated reference data. Model predictions will be compared with manually annotated reference data for dental caries. Accuracy will be reported as a single percentage value (%).
Time frame: Baseline
Sensitivity of the Artificial Intelligence Model for Dental Caries Detection
Sensitivity will be calculated as the proportion of correctly identified positive findings among all positive findings in the expert-annotated reference data. Model predictions will be compared with manually annotated reference data for dental caries. Sensitivity will be reported as a single percentage value (%).
Time frame: Baseline
Specificity of the Artificial Intelligence Model for Dental Caries Detection
Specificity will be calculated as the proportion of correctly identified negative findings among all negative findings in the expert-annotated reference data. Model predictions will be compared with manually annotated reference data for dental caries. Sensitivity will be reported as a single percentage value (%).
Time frame: Baseline
Precision of the Artificial Intelligence Model for Dental Caries Detection
Precision will be calculated as the proportion of correct positive predictions among all positive predictions made by the artificial intelligence model. Model predictions will be compared with manually annotated reference data for dental caries. Precision will be reported as a single percentage value (%).
Time frame: Baselina
F1-Score for Dental Caries Detection
The F1-score will be calculated as the harmonic mean of precision and recall. Values range from 0 to 1, with higher values indicating better detection performance. Model predictions will be compared with manually annotated reference data for dental caries. The F1-score will be reported as a single percentage value (%).
Time frame: Baseline
Intersection over Union (IoU) of the Artificial Intelligence Model for Dental Caries Segmentation
Intersection over Union (IoU) will be calculated as the ratio of the overlapping area to the combined area of the predicted and expert-annotated segmentation masks. Values range from 0 to 1, with higher values indicating better segmentation performance. Model predictions will be compared with manually annotated reference data for dental caries.
Time frame: Baseline
Accuracy of the artificial intelligence model for dental plaque detection
Accuracy will be calculated as the proportion of correct predictions among all evaluated predictions, using expert-annotated reference data. Model predictions will be compared with manually annotated reference data for dental plaque.
Time frame: Baseline
Sensitivity of the Artificial Intelligence Model for Dental Plaque Detection
Sensitivity will be calculated as the proportion of correctly identified positive findings among all positive findings in the expert-annotated reference data. Model predictions will be compared with manually annotated reference data for dental plaque.
Time frame: Baseline
Specificity of the Artificial Intelligence Model for Dental Plaque Detection
Specificity will be calculated as the proportion of correctly identified negative findings among all negative findings in the expert-annotated reference data. Model predictions will be compared with manually annotated reference data for dental plaque.
Time frame: Baseline
Precision of the Artificial Intelligence Model for Dental Plaque Detection
Precision will be calculated as the proportion of correct positive predictions among all positive predictions made by the artificial intelligence model. Model predictions will be compared with manually annotated reference data for dental plaque.
Time frame: Baseline
F1-Score of the Artificial Intelligence Model for Dental Caries Detection
The F1-score will be calculated as the harmonic mean of precision and recall. Values range from 0 to 1, with higher values indicating better detection performance. Model predictions will be compared with manually annotated reference data for dental plaque.
Time frame: Baseline
Intersection over Union (IoU) of the Artificial Intelligence Model for Dental Plaque Segmentation
Intersection over Union (IoU) will be calculated as the ratio of the overlapping area to the combined area of the predicted and expert-annotated segmentation masks. Values range from 0 to 1, with higher values indicating better segmentation performance. Model predictions will be compared with manually annotated reference data for dental plaque.
Time frame: Baseline
AI-integrated caries risk classification
Participants will be classified into low, moderate, or high caries risk categories according to predefined AI-integrated criteria, combining AI-derived dental caries and plaque findings with clinical indices and behavioral risk factors. The outcome will be reported as the number of participants in each risk category.
Time frame: At baseline assessment.
Change in Number of Carious Lesions During Follow-up
The number of carious lesions will be assessed at baseline and follow-up using clinical examinations and digital intraoral images. The change will be calculated by subtracting the baseline number of carious lesions from the follow-up number for each participant. Follow-up assessments will be performed according to the participant's caries risk category.
Time frame: Baseline and up to 6 months
Change in Dental Plaque Percentage During Follow-up
The change in the percentage of tooth surface area covered by dental plaque from baseline to follow-up will be evaluated using digital intraoral images and the artificial intelligence model. Follow-up assessments will be performed according to the participant's caries risk category.
Time frame: Baseline and up to 6 months
Change in Caries Risk Category During Follow-up
Changes in caries risk classification from baseline to follow-up will be evaluated using the predefined low, moderate, and high caries risk categories. The outcome will be reported as the number of participants whose risk category changes during follow-up.
Time frame: Baseline and up to 6 months
Plan to share: Undecided
No publications or documents are linked to this record.
Start is estimated for Jan 2027; primary completion for Dec 2027, 11 months later.
Of 46 non-industry observational studies under Dental Caries, half reached primary completion within 12 months of starting (the median), counting those still running; a quarter within 7 and three quarters within 32. This study's planned 11 months is close to that.
Counting every sponsor, half of 47 studies reached it within 12 months; a quarter within 7 and three quarters within 32.
How these figures are worked out, and which studies are left out of them.
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
This study is not yet recruiting, as verified in Nov 2026. You cannot join it, but the record below documents what was studied.
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