Comparative Accuracy of AI Models and Clinical Assessment for Dental Plaque Detection in Children

January 3, 2025 updated by: Naema Ahmed

Accuracy of Dental Plaque Detection From Intraoral Images Using Different Artificial Intelligence Models Versus Clinical Assessment Among a Group of Children: A Diagnostic Accuracy Study.

This diagnostic accuracy study aims to evaluate the effectiveness of various artificial intelligence models in detecting dental plaque from intraoral images compared to clinical assessments performed by dentists among children. The study seeks to determine the accuracy, sensitivity, specificity, and overall performance of AI technologies in identifying dental plaque. study study Design: Observational study

Study Overview

Status

Status

Indicates the current recruitment status or the expanded access status.
Not yet recruiting

Conditions

Conditions

The disease, disorder, syndrome, illness, or injury that is being studied. On ClinicalTrials.gov, conditions may also include other health-related issues, such as lifespan, quality of life, and health risks.

Intervention / Treatment

Intervention / Treatment

A process or action that is the focus of a clinical study. Interventions include drugs, medical devices, procedures, vaccines, and other products that are either investigational or already available. Interventions can also include noninvasive approaches, such as education or modifying diet and exercise.

Detailed Description

Study Title:

Accuracy of Dental Plaque Detection from Intraoral Images Using Different Artificial Intelligence Models Versus Clinical Assessment Among a Group of Children: A Diagnostic Accuracy Study

Study Overview:

This observational diagnostic accuracy study is designed to evaluate the performance of multiple artificial intelligence (AI) models in detecting dental plaque from intraoral images, compared to traditional clinical assessments conducted by qualified dentists. The primary focus is on pediatric patients, as early detection and management of dental plaque are crucial for maintaining oral health in children.

Background and Rationale:

Dental plaque is a biofilm that forms on teeth and can lead to caries and periodontal disease if not properly managed. Traditional methods of plaque detection rely on visual assessments by dental professionals, which can be subjective and may vary in accuracy. Recent advancements in AI and image processing present an opportunity to enhance the detection and quantification of dental plaque through intraoral images, potentially providing a more objective and efficient assessment tool.

Objectives:

To compare the accuracy of AI models in detecting dental plaque against clinical assessments.

To determine the sensitivity, specificity, and overall diagnostic performance of the AI technologies.

To analyze the potential for AI models to be integrated into routine dental examinations for pediatric patients.

Methodology:

Participants: A sample of pediatric patients will be recruited, ensuring a diverse representation of various demographics and dental health statuses.

Image Acquisition: Intraoral images will be captured using standardized imaging protocols to ensure consistency. High-resolution images will be obtained under controlled conditions to minimize variability.

AI Models: Various AI algorithms, including convolutional neural networks (CNNs) and deep learning techniques, will be trained using a dataset of annotated intraoral images. These models will be evaluated based on their ability to identify and quantify dental plaque.

Clinical Assessment: Trained dentists will perform clinical examinations using standard plaque indices to assess the presence and severity of dental plaque in the same cohort of children.

Data Analysis: Statistical methods will be employed to compare the diagnostic accuracy of AI models with clinical assessments, including calculations of sensitivity, specificity, positive predictive value, and negative predictive value.

Expected Outcomes:

The study aims to elucidate the role of AI in enhancing the detection of dental plaque in children, potentially leading to improved preventive care and treatment strategies. The findings may also contribute to the development of AI-assisted tools for dental practitioners.

Ethical Considerations:

This study will adhere to ethical guidelines, ensuring informed consent is obtained from legal guardians of pediatric participants. Approval from the relevant institutional review board (IRB) will be secured prior to the commencement of the study

Study Type

Study Type

Describes the nature of a clinical study. Study types include interventional studies (also called clinical trials), observational studies (including patient registries), and expanded access.
Observational

Enrollment (Estimated)

Enrollment

The number of participants in a clinical study. The "anticipated" enrollment is the target number of participants that the researchers need for the study.
323

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact

The name and contact information for the person who can answer enrollment questions for a clinical study. Each location where the study is being conducted may also have a specific contact, who may be better able to answer those questions.

Study Contact Backup

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Eligibility Criteria

The key requirements that people who want to participate in a clinical study must meet or the characteristics they must have. Eligibility criteria consist of both inclusion criteria (which are required for a person to participate in the study) and exclusion criteria (which prevent a person from participating). Types of eligibility criteria include whether a study accepts healthy volunteers, has age or age group requirements, or is limited by sex.

Ages Eligible for Study

  • Child

Accepts Healthy Volunteers

N/A

Sampling Method

Non-Probability Sample

Study Population

Children from 7 to 12 years old.

Description

Inclusion Criteria:

.Study participants: Children within age range (7-12) years old. .Teeth without metal crowns or amalgam restoration.

Exclusion Criteria:

  • Children with developmental enamel defects
  • Children who are unwilling to cooperate or who has mental retardation and are prohibited from having their images taken. .Children who's their legal guardians will not approve to participate in the study.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort

Group / Cohort

A group or subgroup of participants in an observational study that is assessed for biomedical or health outcomes.
Intervention / Treatment

Intervention / Treatment

A process or action that is the focus of a clinical study. Interventions include drugs, medical devices, procedures, vaccines, and other products that are either investigational or already available. Interventions can also include noninvasive approaches, such as education or modifying diet and exercise.
intraoral images for Children with Dental Plaque for assessment by dentist

Intervention Overview: Participants will undergo intraoral imaging using [intraoral camera].

Intervention Overview: A trained dentist or dental hygienist will conduct a clinical assessment of each child's dental plaque levels using standard clinical criteria.

Assessment Method: The clinical assessment will involve visual inspection and may use plaque index to evaluate the amount of plaque present.

Data Collection and Analysis:

Outcome Measures: The results from the AI models and clinical assessments will be compared to calculate diagnostic accuracy metrics, such as sensitivity, specificity, positive predictive value, and negative predictive value.
intraoral images for Children with Dental Plaque for assessment by AI models

Intervention Overview: Participants will undergo intraoral imaging using [intraoral camera].

AI Models: The images will be analyzed using different AI models designed for dental plaque detection.

Data Collection and Analysis:

Outcome Measures: The results from the AI models and clinical assessments will be compared to calculate diagnostic accuracy metrics, such as sensitivity, specificity, positive predictive value, and negative predictive value.
Diagnostic test: Dental Plaque Detection Using AI Models

  1. AI Model Analysis:

    Description: Intraoral images of participants will be captured using standardized imaging techniques. These images will then be analyzed using various artificial intelligence models specifically designed for detecting dental plaque. The AI models will process the images to identify and quantify the presence of dental plaque.

  2. Clinical Assessment:

Description: A qualified dentist will perform a traditional clinical examination of the participants to assess dental plaque using standard examination techniques. This will serve as the reference standard against which the AI models will be compared.

Study Procedures Image Acquisition: Intraoral images will be taken of each participant using [ intraoral camera].

AI Model Evaluation: The captured images will be analyzed using different AI algorithms, which may include.

What is the study measuring?

Primary Outcome Measures

Primary Outcome Measures

In a clinical study's protocol, the planned outcome measure that is the most important for evaluating the effect of an intervention/treatment. Most clinical studies have one primary outcome measure, but some have more than one.
Outcome Measure
Measure Description
Time Frame
accuracy of dental plaque detection
Time Frame: primary outcome will be assessed at Baseline Prior to any intervention, intraoral images will be captured and assessed using AI models and clinical evaluation.
The primary outcome measure evaluates the diagnostic accuracy of different artificial intelligence models in detecting dental plaque from intraoral images compared to clinical assessments.
primary outcome will be assessed at Baseline Prior to any intervention, intraoral images will be captured and assessed using AI models and clinical evaluation.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Sponsor

The organization or person who initiates the study and who has authority and control over the study.
Naema Ahmed

Investigators

Investigators

A researcher involved in a clinical study. Related terms include site principal investigator, site sub-investigator, study chair, study director, and study principal investigator.
  • Study Director: Cairo University, Cairo University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

Study Start

The actual date on which the first participant was enrolled in a clinical study. The "anticipated" study start date is the date that the researchers think will be the study start date.
January 1, 2025

Primary Completion (Estimated)

Primary Completion

The date on which the last participant in a clinical study was examined or received an intervention to collect final data for the primary outcome measure. Whether the clinical study ended according to the protocol or was terminated does not affect this date. For clinical studies with more than one primary outcome measure with different completion dates, this term refers to the date on which data collection is completed for all the primary outcome measures. The "anticipated" primary completion date is the date that the researchers think will be the primary completion date for the study.
December 30, 2025

Study Completion (Estimated)

Study Completion

The date on which the last participant in a clinical study was examined or received an intervention/treatment to collect final data for the primary outcome measures, secondary outcome measures, and adverse events (that is, the last participant's last visit). The "anticipated" study completion date is the date that the researchers think will be the study completion date.
December 30, 2025

Study Registration Dates

First Submitted

First Submitted

The date on which the study sponsor or investigator first submitted a study record to ClinicalTrials.gov. There is typically a delay of a few days between the first submitted date and the record's availability on ClinicalTrials.gov (the first posted date).
December 22, 2024

First Submitted That Met QC Criteria

First Submitted That Met QC Criteria

The date on which the study sponsor or investigator first submits a study record that is consistent with National Library of Medicine (NLM) quality control (QC) review criteria. The sponsor or investigator may need to revise and submit a study record one or more times before NLM's QC review criteria are met. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
January 3, 2025

First Posted (Actual)

First Posted

The date on which the study record was first available on ClinicalTrials.gov after National Library of Medicine (NLM) quality control (QC) review has concluded. There is typically a delay of a few days between the date the study sponsor or investigator submitted the study record and the first posted date.
March 25, 2025

Study Record Updates

Last Update Posted (Actual)

Last Update Posted

The most recent date on which changes to a study record were made available on ClinicalTrials.gov. There may be a delay between when the changes were submitted to ClinicalTrials.gov by the study's sponsor or investigator (the last update submitted date) and the last update posted date.
March 25, 2025

Last Update Submitted That Met QC Criteria

Last Update Submitted That Met QC Criteria

The most recent date on which the study sponsor or investigator submitted changes to a study record that are consistent with National Library of Medicine (NLM) quality control (QC) review criteria. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
January 3, 2025

Last Verified

Last Verified

The most recent date on which the study sponsor or investigator confirmed the information about a clinical study on ClinicalTrials.gov as accurate and current. If a study with a recruitment status of recruiting; not yet recruiting; or active, not recruiting has not been confirmed within the past 2 years, the study's recruitment status is shown as unknown.
January 1, 2025

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

Other Study ID Numbers

Identifiers or ID numbers other than the NCT number that are assigned to a clinical study by the study's sponsor, funders, or others. These numbers may include unique identifiers from other trial registries and National Institutes of Health grant numbers.
  • OP7-1-1

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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