AI-Based Caries and Plaque Detection and Caries Risk Prediction in Children
NCT07929753, Not yet recruiting, Marmara University
Trial 9
As registered on ClinicalTrials.gov, in the graph's capture of the registry.
| Field | Value | Source |
|---|---|---|
| Title | AI-Based Caries and Plaque Detection and Caries Risk Prediction in Children | ClinicalTrials.gov |
| NCT number | NCT07929753 | ClinicalTrials.gov |
| Status | Not yet recruiting | ClinicalTrials.gov |
| Enrollment | 250 participants, estimated | ClinicalTrials.gov |
| Lead sponsor | Marmara University | ClinicalTrials.gov |
| Conditions | Dental Caries, Caries Risk, Dental Plaque | ClinicalTrials.gov |
| Interventions | individualized preventive recommendations | ClinicalTrials.gov |
| First posted | 2026-11-10 | ClinicalTrials.gov |
| Last updated | 2026-11-10 | ClinicalTrials.gov |
Registered outcomes 16
Each outcome the trial registered, primary first, in ClinicalTrials.gov's words.
| List | Outcome | Time frame | Description | Source |
|---|---|---|---|---|
| Primary | Accuracy of the Artificial Intelligence Model for Dental Caries Detection | Baseline | 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 (%). | ClinicalTrials.gov |
| Primary | Sensitivity of the Artificial Intelligence Model for Dental Caries Detection | Baseline | 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 (%). | ClinicalTrials.gov |
| Primary | Specificity of the Artificial Intelligence Model for Dental Caries Detection | Baseline | 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 (%). | ClinicalTrials.gov |
| Primary | Precision of the Artificial Intelligence Model for Dental Caries Detection | Baselina | 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 (%). | ClinicalTrials.gov |
| Primary | F1-Score for Dental Caries Detection | Baseline | 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 (%). | ClinicalTrials.gov |
| Primary | Intersection over Union (IoU) of the Artificial Intelligence Model for Dental Caries Segmentation | Baseline | 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. | ClinicalTrials.gov |
| Primary | Accuracy of the artificial intelligence model for dental plaque detection | Baseline | 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. | ClinicalTrials.gov |
| Primary | Sensitivity of the Artificial Intelligence Model for Dental Plaque Detection | Baseline | 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. | ClinicalTrials.gov |
| Primary | Specificity of the Artificial Intelligence Model for Dental Plaque Detection | Baseline | 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. | ClinicalTrials.gov |
| Primary | Precision of the Artificial Intelligence Model for Dental Plaque Detection | Baseline | 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. | ClinicalTrials.gov |
| Primary | F1-Score of the Artificial Intelligence Model for Dental Caries Detection | Baseline | 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. | ClinicalTrials.gov |
| Primary | Intersection over Union (IoU) of the Artificial Intelligence Model for Dental Plaque Segmentation | Baseline | 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. | ClinicalTrials.gov |
| Secondary | AI-integrated caries risk classification | At baseline assessment. | 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. | ClinicalTrials.gov |
| Secondary | Change in Number of Carious Lesions During Follow-up | Baseline and up to 6 months | 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. | ClinicalTrials.gov |
| Secondary | Change in Dental Plaque Percentage During Follow-up | Baseline and up to 6 months | 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. | ClinicalTrials.gov |
| Secondary | Change in Caries Risk Category During Follow-up | Baseline and up to 6 months | 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. | ClinicalTrials.gov |
Outcomes 0
Each outcome the trial posted results for, in ClinicalTrials.gov's words.
| Type | Outcome | Time frame | Population | Units | Measured as | Source |
|---|
Results by group 0
Each measurement the trial posted, group by group, in the words and numbers it posted.
| Outcome | Group | Category | Value | Spread | Units | Source |
|---|
Participant flow 0
How many started, completed or left each group, period by period.
| Period | Milestone | Group | Participants | Units counted | Source |
|---|
Adverse events 0
Each adverse event the trial posted, group by group: the participants affected out of those at risk.
| Type | Event | Organ system | Group | Affected | At risk | Events | Source |
|---|
Adverse event totals 0
The totals the trial posted for each group.
| Total | Group | Affected | At risk | Source |
|---|
Papers 0
The papers the trial lists on ClinicalTrials.gov.
| PMID | Listed as | Source |
|---|
Grants 0
The grants NIH RePORTER links to the trial, newest funded first: NIH's, and some of AHRQ's, CDC's, FDA's and VA's. Each shows its title, principal investigators, organization and funder in its newest year.
| Project | Title | Principal investigators | Organization | Funder | Fiscal years | Source |
|---|
Trial comparisons 0
Comparisons of the trial's two groups, calculated by ConductScience from the counts it posted.
| Comparison | Source |
|---|
Tests 0
The ConductCognition tests this trial's registered outcomes name, as ScienceGraph matched them in trial-tests-2026-11-12-0.2.1.
| Test | Named as | Source |
|---|
Source 5
| Field | Value | Source |
|---|---|---|
| Release | trials-2026-11-12-0.4.0 | ClinicalTrials.gov |
| Release manifest SHA-256 | 0f4a8d8c48aee2a7573ae0fa94ecbeab70c1adbe449bbc95c424bc7d3e97b9d4 | ClinicalTrials.gov |
| Registry capture | 2026-11-12 | ClinicalTrials.gov |
| Results copy, AACT | 2026-03-23 | ClinicalTrials.gov |
| NIH clinical studies file | 2026-11-13 | ClinicalTrials.gov |
Release trials-2026-11-12-0.4.0
ClinicalTrials.gov's posted results, through CTTI's AACT: ClinicalTrials.gov data via Aggregate Analysis of ClinicalTrials.gov (AACT), CTTI. CTTI makes its materials freely available and requests acknowledgement. https://aact.ctti-clinicaltrials.org/documentation/299 ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.
ClinicalTrials.gov: none stated ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.
NIH RePORTER's clinical studies and project files: Public domain (NIH RePORTER)