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Creating and Testing an Online Self-Assessment Tool to Identify Periodontal Disease Risk in Saudi Adults Using Advanced Learning Methods
Led by Universiti Sains Malaysia · Updated on 2026-05-05
393
Participants Needed
1
Research Sites
8 weeks
Total Duration
AI-Summary
What this Trial Is About
Periodontal disease is a common oral health issue that can silently worsen, causing gum inflammation, tooth loss, and reduced quality of life if not detected early. This research aims to evaluate how accurately an online self-assessment tool can identify periodontal risk among adults by comparing questionnaire results with clinical periodontal examinations. The study focuses on adults in the general population and seeks to improve early detection and preventive care for periodontal disease. Participants will complete an online questionnaire about their oral health symptoms, dental history, oral hygiene habits, and other relevant factors. They will also undergo a clinical periodontal examination during the same visit, performed by a trained clinician. The clinical exam will categorize participants into low, medium, or high periodontal risk groups, serving as the reference standard to assess the questionnaires accuracy. No treatment or preventive intervention is given as part of this observational study. During the study visit, participants will fill out the online questionnaire and receive the clinical examination. Researchers will measure how well the questionnaire-based risk categories agree with the clinical findings using diagnostic accuracy methods, including sensitivity, specificity, and agreement statistics. Minimal risks include mild discomfort or minor bleeding during the exam and privacy considerations. Participants data will be securely stored with identifiers to protect confidentiality. The study results aim to support improving the self-assessment tool for better community-level oral health prevention.
CONDITIONS
Brief Title
Development of A Self-Assessment Tool for Personalized Preventive Periodontal Care Among the Saudi Population Using Semi-Supervised Learning
Research Team
A
Abdulrahman Ahmed Alshehri, MS
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