Diagnostic Accuracy of Oral Images, OPGs, and Questionnaires vs. Clinical Assessment for Periodontal Disease
NCT07164573 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 2000
Last updated 2025-09-10
Summary
This is a multi-center, cross-sectional diagnostic study aimed at evaluating the accuracy of various non-invasive methods-including self-reported questionnaires, intra-oral photographs, smartphone images, intraoral scans (IOS), and orthopantomographs (OPGs)-in detecting periodontal health and disease, compared to clinical periodontal examination as the gold standard. The study will enroll 2,000 subjects across five centers, representing the full spectrum of periodontal conditions (health, gingivitis, and periodontitis stages I-IV). Participants will undergo a standardized clinical examination, radiographic imaging, and complete validated questionnaires. Machine learning models (e.g., HC-Net+ for OPGs and DLM for oral image) will be used to analyze images and integrate data domains. The primary outcome is the diagnostic accuracy (sensitivity, specificity, AUROC) of each method alone and in combination for classifying periodontal status. The study aims to validate and refine AI-based tools for scalable, efficient periodontal screening in clinical and community settings.
Conditions
- Periodontal Diseases
- Gingivitis
- Periodontitis
Sponsors & Collaborators
-
University of Chieti
collaborator OTHER -
Department of Medical Sciences, University of Torino
collaborator UNKNOWN - collaborator OTHER
-
University of Roma La Sapienza
collaborator OTHER -
Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University
lead OTHER
Eligibility
- Min Age
- 18 Years
- Sex
- ALL
- Healthy Volunteers
- Yes
Timeline & Regulatory
- Start
- 2025-11-13
- Primary Completion
- 2028-11-13
- Completion
- 2028-11-13
Countries
- China
Study Locations
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