Ignite Creation Date:
2026-03-26 @ 3:18 PM
Ignite Modification Date:
2026-04-04 @ 5:22 PM
Study NCT ID:
NCT07406867
Status:
NOT_YET_RECRUITING
Last Update Posted:
2026-02-12
First Post:
2026-02-05
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Diagnostic Accuracy of Oral Images, OPGs, Biomarkers and Questionnaires vs. Clinical Assessment for Periodontal Disease (PostNCT07164573)
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D010510', 'term': 'Periodontal Diseases'}, {'id': 'D005891', 'term': 'Gingivitis'}, {'id': 'D010518', 'term': 'Periodontitis'}, {'id': 'D004194', 'term': 'Disease'}], 'ancestors': [{'id': 'D009059', 'term': 'Mouth Diseases'}, {'id': 'D009057', 'term': 'Stomatognathic Diseases'}, {'id': 'D007239', 'term': 'Infections'}, {'id': 'D005882', 'term': 'Gingival Diseases'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITH_DNA', 'description': 'Unstimulated saliva (5 mL), oral rinse, and subgingival plaque samples collected from first molars. Samples will be collected at the Shanghai center only for biomarker analysis including protein and microbial signatures.'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 2000}, 'targetDuration': '1 Day', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2026-03-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-02', 'completionDateStruct': {'date': '2029-03-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-02-05', 'studyFirstSubmitDate': '2026-02-05', 'studyFirstSubmitQcDate': '2026-02-05', 'lastUpdatePostDateStruct': {'date': '2026-02-12', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-02-12', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2029-03-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Diagnostic accuracy for detecting periodontitis (Stage II-IV) as determined by the Area Under the Receiver Operating Characteristic Curve (AUROC) of each index test against the clinical reference standard', 'timeFrame': 'Cross-sectional (assessed at the day 1 of participant enrollment)', 'description': '1. Diagnostic accuracy of the AI-based analysis of OPGs (HC-Net+) for detecting periodontitis (Stage II-IV)\n2. Diagnostic accuracy of the AI-based analysis of intra-oral photographs for detecting periodontitis (Stage II-IV)\n3. Diagnostic accuracy of the self-reported questionnaire (modified CDC-AAP) for detecting periodontitis (Stage II-IV)\n4. Diagnostic accuracy of salivary biomarker-based classifiers (specific proteins obtained from unstimulated saliva and oral rinse) for detecting periodontitis (Stage II-IV)\n5. Diagnostic accuracy of microbial biomarker-based classifiers (microbial signatures obtained from subgingival plaque) for detecting periodontitis (Stage II-IV)\n6. Diagnostic accuracy of combined multi-modal algorithm integrating questionnaires, oral images, OPGs, and biomarkers for detecting periodontitis (Stage II-IV)'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Diagnosis', 'ArtiLcial Intelligence', 'Periodontal Diseases', 'Radiography', 'Machine Learning', 'Photography'], 'conditions': ['Periodontal Disease', 'Gingivitis', 'Periodontitis']}, 'descriptionModule': {'briefSummary': 'This multi-center, cross-sectional diagnostic trial evaluates the accuracy of multiple non-invasive screening tools-including self-reported questionnaires, intra-oral photographs, orthopantomographs (OPGs), intraoral scans (IOS), and salivary/microbial biomarkers-for detecting periodontal health and diseases (gingivitis and periodontitis Stages I-IV), using full-mouth clinical periodontal examination as the reference standard. A total of 2,000 participants will be recruited across five international centers. Diagnostic performance (sensitivity, specificity, AUROC) of individual and combined methods will be assessed using logistic regression and machine learning algorithms to establish an optimized multi-modal screening algorithm.', 'detailedDescription': 'This study is an extension of NCT07164573, with the addition of salivary and microbial biomarker analysis as index tests. While NCT07164573 focuses on questionnaires, oral images, and OPGs, this study incorporates biomarker-based classifiers to evaluate a comprehensive multi-modal diagnostic approach for periodontal disease detection.This is a multi-center, cross-sectional diagnostic accuracy study. The study aims to validate and compare the performance of multiple index tests against a clinical reference standard for the detection of periodontal health and disease. The reference standard for periodontal diagnosis will be a comprehensive full-mouth periodontal examination conducted by trained and calibrated examiners at five international clinical centers. Diagnoses (periodontal health, gingivitis, periodontitis Stages I-IV) will be assigned based on the integration of clinical, radiographic, and demographic data according to the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions. The decision-making algorithms proposed by Tonetti and Sanz (2019) will be applied. The index tests under investigation include: 1. A set of self-reported questionnaires, including a modified CDC-AAP questionnaire, OHIP-14, and a dietary survey. 2. Intra-oral clinical photographs captured with a professional camera and a smartphone. 3. A self-performed intra-oral photograph ("selfie"), with and without cheek retractors. 4. Digital orthopantomographs (OPGs). 5. Intraoral scans (IOS). 6. Biomarker analysis of specific proteins and microbial signatures obtained from unstimulated saliva, oral rinse, and subgingival plaque (collected at the Shanghai center only). Data from the index tests will be analyzed using previously developed and validated machine learning models (e.g., HC-Net+ for OPG analysis, a deep learning model for single frontal-view images, and biomarker-based classifiers for periodontal disease detection). The data collected in this study will also be used to further refine these models, particularly to improve the differentiation between gingivitis/Stage I periodontitis and health/Stage II-IV periodontitis.\n\nThe primary analytical method will involve assessing the diagnostic accuracy of each index test, both individually and in combination, by calculating sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) against the clinical reference standard. Logistic regression and machine learning algorithms will be employed to identify the most predictive variables and optimal diagnostic sequences. A total of 2,000 participants will be recruited across the five centers. The study will be conducted in compliance with the Declaration of Helsinki, ICH-GCP guidelines, and relevant STARD and AI-specific reporting guidelines.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'The study population will consist of a convenience sample of consecutive adult patients seeking routine dental care across Lve participating dental centers. We aim to enroll a total of 2000 participants, representing the full spectrum of periodontal conditions (i.e., periodontal health, gingivitis, and periodontitis stages I through IV) based on the reference clinical examination.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1\\. Adult patients aged 18 years or older.2. Seeking dental care at one of the participating study centers.3. Ability to understand and willingness to provide written informed consent.\n\nExclusion Criteria:\n\n1\\. Edentulous patients (complete tooth loss).2. Pregnancy or lactation.3. History of periodontal therapy (other than supragingival prophylaxis/cleaning) within the past 12 months.4. Use of antibiotic medication within the 3 months prior to enrollment.'}, 'identificationModule': {'nctId': 'NCT07406867', 'briefTitle': 'Diagnostic Accuracy of Oral Images, OPGs, Biomarkers and Questionnaires vs. Clinical Assessment for Periodontal Disease (PostNCT07164573)', 'organization': {'class': 'OTHER', 'fullName': "Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University"}, 'officialTitle': 'Diagnostic Accuracy of Oral Images, Orthopantomographs (OPGs), Biomarkers and Self-Reported Questionnaires vs. Clinical Assessment for Detecting Periodontal Health and Disease: a Multi-center Diagnostic Study', 'orgStudyIdInfo': {'id': 'SH9H-2025-T363-2'}, 'secondaryIdInfos': [{'id': 'Post-NCT07164573', 'type': 'OTHER', 'domain': "Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University"}]}, 'armsInterventionsModule': {'armGroups': [{'label': 'All Participants'}]}, 'contactsLocationsModule': {'locations': [{'zip': '200000', 'city': 'Shanghai', 'state': 'Shanghai Municipality', 'country': 'China', 'facility': "Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine", 'geoPoint': {'lat': 31.22222, 'lon': 121.45806}}], 'centralContacts': [{'name': 'Maurizio S. Tonetti', 'role': 'CONTACT', 'email': 'tonetti@hku.hk', 'phone': '15000102368'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'The individual participant data collected in this study contains highly sensitive personal health information. The informed consent obtained from participants did not include provisions for public sharing of their individual-level data. Making this data publicly available could compromise participant privacy and confidentiality, which are our primary ethical obligations. Furthermore, the data is part of an ongoing research program focused on the development and validation of artificial intelligence models. The complete datasets are complex and require specialized knowledge for appropriate analysis and interpretation. Aggregated, de-identified results will be made available in published manuscripts and supplementary materials.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': "Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University", 'class': 'OTHER'}, 'collaborators': [{'name': 'University of Chieti', 'class': 'OTHER'}, {'name': "King's College London", 'class': 'OTHER'}, {'name': 'University of Roma La Sapienza', 'class': 'OTHER'}, {'name': 'University of Turin, Italy', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Maurizio Tonetti', 'investigatorAffiliation': "Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University"}}}}