Periodontal DiseasesNCT07164573
Diagnostic Accuracy of Oral Images, OPGs, and Questionnaires vs. Clinical Assessment for Periodontal Disease
An observational study of Periodontal Diseases and Gingivitis, run by Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University at 1 site in China.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Periodontal Diseases, Gingivitis, Periodontitis
- How many people
- 2,000 participants
- Where
- 1 siteChina
- Study dates
- 13 Nov 2025to 13 Nov 2028
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The study population will consist of a convenience sample of consecutive adult patients seeking routine dental care across five 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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine
Shanghai, Shanghai Municipality
Maurizio S. Tonetti, 15000102368, tonetti@hku.hk
About this study
Official title: Diagnostic Accuracy of Oral Images, Orthopantomographs (OPGs) and Self-Reported Questionnaires vs. Clinical Assessment for Detecting Periodontal Health and Disease: a Multi-center Diagnostic Study
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.
Read the detailed description
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. 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:
- A set of self-reported questionnaires, including a modified CDC-AAP questionnaire.
- Intra-oral clinical photographs captured with a professional camera and a smartphone.
- A self-performed intra-oral photograph ("selfie").
- Digital orthopantomographs (OPGs).
- Intraoral scans (IOS). 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). 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.
The 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.
The study will be conducted in compliance with the Declaration of Helsinki, ICH-GCP guidelines, and relevant STARD and AI-specific reporting guidelines.
- Conditions
- Periodontal Diseases, Gingivitis, Periodontitis
- Keywords
- Diagnosis, Artificial Intelligence, Periodontal Diseases, Radiography, Machine Learning, Photography
- Medical subject headings
- Periodontal Diseases, Gingivitis, Periodontitis, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| All Participants | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
- Diagnostic accuracy of the AI-based analysis of OPGs (HC-Net+) for detecting periodontitis (Stage II-IV)
- Diagnostic accuracy of the AI-based analysis of intra-oral photographs for detecting periodontitis (Stage II-IV)
- Diagnostic accuracy of the self-reported questionnaire (modified CDC-AAP) for detecting periodontitis (Stage II-IV)
Measured: Cross-sectional (assessed at the day 1 of participant enrollment)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Sep 2025
- Study start13 Nov 2025 (estimated)
- Primary completion13 Nov 2028 (estimated)
- Study completion13 Nov 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Periodontal DiseasesStudies of the same condition, open and under way.
- More trials run by Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 2,000 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- University of Chieti, Department of Medical Sciences, University of Torino, King's College London, University of Roma La Sapienza
- Responsible party
- Principal investigator, Maurizio Tonetti, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07164573
- Sponsor's protocol ID
- SH9H-2025-T363-1
- Status last verified
- Sep 2025
- First submitted
- 2 Sep 2025
- Last updated
- 10 Sep 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
Study at a glance
From the registry record, last updated by the sponsor on 10 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Diagnostic Accuracy of Oral Images, Orthopantomographs (OPGs) and Self-Reported Questionnaires vs. Clinical Assessment for Detecting Periodontal Health and Disease: a Multi-center Diagnostic Study
- ClinicalTrials.gov ID
- NCT07164573
- Acronym / sponsor ID
- SH9H-2025-T363-1
- Conditions
- Periodontal Diseases, Gingivitis, Periodontitis
- Keywords
- Diagnosis, Artificial Intelligence, Periodontal Diseases, Radiography, Machine Learning, Photography
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong UniversityUniversity, hospital or other
- Collaborators
- University of Chieti, Department of Medical Sciences, University of Torino, King's College London, University of Roma La Sapienza
- Study start
- 13 Nov 2025 (estimated)
- Primary completion
- 13 Nov 2028 (estimated)
- Study completion
- 13 Nov 2028 (estimated)
- First posted
- 10 Sep 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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. 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:
- A set of self-reported questionnaires, including a modified CDC-AAP questionnaire.
- Intra-oral clinical photographs captured with a professional camera and a smartphone.
- A self-performed intra-oral photograph ("selfie").
- Digital orthopantomographs (OPGs).
- Intraoral scans (IOS). 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). 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.
The 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.
The study will be conducted in compliance with the Declaration of Helsinki, ICH-GCP guidelines, and relevant STARD and AI-specific reporting guidelines.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Day
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| All Participants | – | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The study population will consist of a convenience sample of consecutive adult patients seeking routine dental care across five 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.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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
Time frame: Cross-sectional (assessed at the day 1 of participant enrollment)
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)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Sep 2025.
- Individual participant dataSharing planned: No. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Periodontal Diseases and Gingivitis, run by Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 10 Sep 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Maurizio S. Tonetti (15000102368, tonetti@hku.hk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome 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 (time frame: Cross-sectional (assessed at the day 1 of participant enrollment)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 10 Sep 2025; AzTrials last refreshed its copy on 8 Nov 2026.