Ignite Creation Date:
2025-12-24 @ 9:20 PM
Ignite Modification Date:
2026-01-02 @ 1:15 PM
Study NCT ID:
NCT06760104
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-01-06
First Post:
2024-12-22
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Comparative Accuracy of AI Models and Clinical Assessment for Dental Plaque Detection in Children
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003773', 'term': 'Dental Plaque'}], 'ancestors': [{'id': 'D003741', 'term': 'Dental Deposits'}, {'id': 'D014076', 'term': 'Tooth Diseases'}, {'id': 'D009057', 'term': 'Stomatognathic Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 323}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-01-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-01', 'completionDateStruct': {'date': '2025-12-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-01-03', 'studyFirstSubmitDate': '2024-12-22', 'studyFirstSubmitQcDate': '2025-01-03', 'lastUpdatePostDateStruct': {'date': '2025-01-06', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-01-06', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-12-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'accuracy of dental plaque detection', 'timeFrame': 'primary outcome will be assessed at Baseline Prior to any intervention, intraoral images will be captured and assessed using AI models and clinical evaluation.', 'description': 'The primary outcome measure evaluates the diagnostic accuracy of different artificial intelligence models in detecting dental plaque from intraoral images compared to clinical assessments.'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Dental Plaque']}, 'descriptionModule': {'briefSummary': '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', 'detailedDescription': 'Study Title:\n\nAccuracy of Dental Plaque Detection from Intraoral Images Using Different Artificial Intelligence Models Versus Clinical Assessment Among a Group of Children: A Diagnostic Accuracy Study\n\nStudy Overview:\n\nThis 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.\n\nBackground and Rationale:\n\nDental 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.\n\nObjectives:\n\nTo compare the accuracy of AI models in detecting dental plaque against clinical assessments.\n\nTo determine the sensitivity, specificity, and overall diagnostic performance of the AI technologies.\n\nTo analyze the potential for AI models to be integrated into routine dental examinations for pediatric patients.\n\nMethodology:\n\nParticipants: A sample of pediatric patients will be recruited, ensuring a diverse representation of various demographics and dental health statuses.\n\nImage 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.\n\nAI 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.\n\nClinical 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.\n\nData 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.\n\nExpected Outcomes:\n\nThe 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.\n\nEthical Considerations:\n\nThis 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'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD'], 'maximumAge': '12 Years', 'minimumAge': '7 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Children from 7 to 12 years old.', 'eligibilityCriteria': "Inclusion Criteria:\n\n.Study participants: Children within age range (7-12) years old. .Teeth without metal crowns or amalgam restoration.\n\nExclusion Criteria:\n\n* Children with developmental enamel defects\n* 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."}, 'identificationModule': {'nctId': 'NCT06760104', 'briefTitle': 'Comparative Accuracy of AI Models and Clinical Assessment for Dental Plaque Detection in Children', 'organization': {'class': 'OTHER', 'fullName': 'Cairo University'}, 'officialTitle': '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.', 'orgStudyIdInfo': {'id': 'OP7-1-1'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'intraoral images for Children with Dental Plaque for assessment by dentist', 'description': "Intervention Overview: Participants will undergo intraoral imaging using \\[intraoral camera\\].\n\nIntervention Overview: A trained dentist or dental hygienist will conduct a clinical assessment of each child's dental plaque levels using standard clinical criteria.\n\nAssessment Method: The clinical assessment will involve visual inspection and may use plaque index to evaluate the amount of plaque present.\n\nData Collection and Analysis:\n\nOutcome 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."}, {'label': 'intraoral images for Children with Dental Plaque for assessment by AI models', 'description': 'Intervention Overview: Participants will undergo intraoral imaging using \\[intraoral camera\\].\n\nAI Models: The images will be analyzed using different AI models designed for dental plaque detection.\n\nData Collection and Analysis:\n\nOutcome 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.', 'interventionNames': ['Diagnostic Test: Dental Plaque Detection Using AI Models']}], 'interventions': [{'name': 'Dental Plaque Detection Using AI Models', 'type': 'DIAGNOSTIC_TEST', 'description': '1. AI Model Analysis:\n\n 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.\n2. Clinical Assessment:\n\nDescription: 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.\n\nStudy Procedures Image Acquisition: Intraoral images will be taken of each participant using \\[ intraoral camera\\].\n\nAI Model Evaluation: The captured images will be analyzed using different AI algorithms, which may include.', 'armGroupLabels': ['intraoral images for Children with Dental Plaque for assessment by AI models']}]}, 'contactsLocationsModule': {'locations': [{'zip': '11511', 'city': 'Cairo', 'country': 'Egypt', 'contacts': [{'name': 'cairo universitty', 'role': 'CONTACT', 'email': 'naema.altrablsi@dentistry.cu.edu.eg', 'phone': '0020238355275'}, {'name': 'Naema Ahmed', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Cairo University', 'geoPoint': {'lat': 30.06263, 'lon': 31.24967}}], 'centralContacts': [{'name': 'Naema Altrablsi', 'role': 'CONTACT', 'email': 'naema.altrablsi@dentistry.cu.edu.eg', 'phone': '00201152442411'}, {'name': 'Hala Mohiey Eldin, Prof. Doctor', 'role': 'CONTACT', 'email': 'hala.mohyeldin@dentistry.cu.edu.eg', 'phone': '00201001459467'}], 'overallOfficials': [{'name': 'Cairo University', 'role': 'STUDY_DIRECTOR', 'affiliation': 'Cairo University'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Naema Ahmed', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR_INVESTIGATOR', 'investigatorTitle': 'naemaahmed', 'investigatorFullName': 'Naema Ahmed', 'investigatorAffiliation': 'Cairo University'}}}}