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.
Adult patients aged 18 years or older.
Seeking dental care at one of the participating study centers.
Ability to understand and willingness to provide written informed consent.
Edentulous patients (complete tooth loss).
Pregnancy or lactation.
History of periodontal therapy (other than supragingival prophylaxis/cleaning) within the past 12 months.
Use of antibiotic medication within the 3 months prior to enrollment.
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.
Cohort
Prospective
Not listed
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
1. Diagnostic accuracy of the AI-based analysis of OPGs (HC-Net+) for detecting periodontitis (Stage II-IV) 2. Diagnostic accuracy of the AI-based analysis of intra-oral photographs for detecting periodontitis (Stage II-IV) 3. Diagnostic accuracy of the self-reported questionnaire (modified CDC-AAP) for detecting periodontitis (Stage II-IV) 4. Diagnostic accuracy of salivary biomarker-based classifiers (specific proteins obtained from unstimulated saliva and oral rinse) for detecting periodontitis (Stage II-IV) 5. Diagnostic accuracy of microbial biomarker-based classifiers (microbial signatures obtained from subgingival plaque) for detecting periodontitis (Stage II-IV) 6. Diagnostic accuracy of combined multi-modal algorithm integrating questionnaires, oral images, OPGs, and biomarkers for detecting periodontitis (Stage II-IV)
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Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University
Lead sponsor
University of Chieti
Collaborator
King's College London
Collaborator
University of Roma La Sapienza
Collaborator
University of Turin, Italy
Collaborator
