Periodontal DiseaseNCT07629934
Artificial Intelligence for Diagnosing Periodontitis and Monitoring Gingival Inflammation
An observational study of Periodontal Disease, run by Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University at 1 site in China.
Trial summary
Background and Objective: Periodontitis and gingivitis are highly prevalent oral diseases that require accurate diagnostic classification and continuous gingival health monitoring. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Periodontal Disease
- What is being tested?
- AI-driven Periodontal Diagnostic and Monitoring Algorithms
- Type of treatment
- Injection or infusion
- How many people
- 900 participants
- Where
- 1 siteChina
- Study dates
- 10 Sep 2025to 10 Sep 2026
- 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 consists of adult patients who sought routine dental care or periodontal evaluation at the primary medical center (including its main campus and an alternative secondary campus) and two independent regional hospitals. This multi-center population reflects a real-world, diverse clinical screening pool of patients presenting with varying degrees of periodontal health, ranging from completely healthy gingiva to severe, advanced periodontitis. Eligible participants are identified based on the availability of concurrent full-mouth clinical periodontal charting and digital panoramic radiographs.
Who may be able to join
- Patients aged > 18 years at the time of their clinical periodontal examination.
- Availability of complete full-mouth periodontal charting records, which must include Probing Depth (PD) and Bleeding on Probing (BOP) documented at 6 sites per tooth.
- Availability of a digital panoramic radiograph of acceptable diagnostic quality, taken within one months of the clinical periodontal examination.
Who cannot join
- Patients who are completely edentulous or those who have undergone full-arch dental implant rehabilitation (not applicable for natural teeth periodontitis staging).
- Panoramic radiographs with severe image degradation, including major motion artifacts, severe positioning errors, or poor contrast/exposure that obscures the alveolar bone crest.
- Presence of extensive metal artifacts or massive bilateral multiple fixed crowns/bridges that completely shadow the marginal bone level of interest.
- Incomplete clinical electronic medical records or missing core diagnostic descriptors required to establish the clinical gold standard for periodontitis staging or gingival inflammation.
Trial locations and contacts
1 site in 1 country.
China1 site
Shanghai Ninth People's Hospital affiliated to Shanghai Jiao Tong University School of Medicine
Shanghai
About this study
Official title: Evaluation of Artificial Intelligence Models for Periodontitis Diagnosis and Gingival Inflammation Monitoring at Tooth and Patient Levels: A Diagnostic Accuracy Study
Background and Objective:
Periodontitis and gingivitis are highly prevalent oral diseases that require accurate diagnostic classification and continuous gingival health monitoring. This study aims to develop, internally validate, and externally evaluate the diagnostic accuracy of artificial intelligence (AI) models for periodontitis staging and gingival inflammation assessment at both tooth and patient levels.
Read more
Study Design:
This is a multi-center observational study utilizing a large-scale primary clinical dataset for model development. To rigorously evaluate the generalizability of the trained AI models, two distinct pathways of independent external validation will be implemented across multiple clinical sites.
Research Phases \& Validation Architecture:
Phase 1 (Periodontitis Diagnosis via Probing): Development of an AI model to diagnose periodontitis (binary classification: stage 0/I vs. stage II/III/IV) at both tooth and patient levels, using comprehensive clinical periodontal probing as the gold standard. External Validation I will be performed using an independent cohort from another campus of the primary hospital to test the model's diagnostic accuracy.
Phase 2 (Periodontitis Diagnosis via Radiographs): Development of an AI model to diagnose periodontitis (binary classification: stage 0/I vs. stage II/III/IV) at both tooth and patient levels, using digital panoramic radiographs as the reference standard. External Validation II will be conducted using distinct, independent image datasets acquired from two separate regional hospitals to evaluate geographic generalizability.
Phase 3 (Gingival Inflammation Monitoring): Development of an AI model to monitor and assess gingival inflammation at both tooth and patient levels, based on Probing Depth (PD) and Bleeding on Probing (BOP) as the gold standard. This model's performance will also be evaluated through External Validation I using the independent dataset from the primary hospital's alternative campus.
Significance:
By validating the AI models across varied institutional workflows and imaging systems, this study will provide high-level evidence on the clinical utility and robustness of AI-driven digital systems for automated periodontal screening and long-term health monitoring.
- Conditions
- Periodontal Disease
- Being tested
- AI-driven Periodontal Diagnostic and Monitoring Algorithms
- Medical subject headings
- Periodontal Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-driven Periodontal Diagnostic and Monitoring AlgorithmsDiagnostic test
The intervention evaluated in this observational study is the deployment of deep learning/artificial intelligence (AI) software models.
The AI algorithms process two streams of standard clinical data to perform three automated diagnostic tasks without altering patient care:
Automated classification of periodontitis stages (Stage 0/I vs. Stage II/III/IV) utilizing full-mouth clinical charting metrics.
Automated classification of periodontitis stages (Stage 0/I vs. Stage II/III/IV) utilizing digital panoramic radiographs.
Automated assessment and monitoring of gingival inflammation flags based on Probing Depth (PD) and Bleeding on Probing (BOP) patterns.
The outputs of these AI models will be directly compared against clinical and radiographic gold standards to calculate diagnostic accuracy metrics.
| Study group | Role | What they receive |
|---|---|---|
| Multi-center Periodontal AI Development and Validation Cohort | Development Dataset (Primary Campus): Large-scale data used for the initial training and internal validation of the AI algorithms.
External Validation Dataset I (Secondary Campus): An independent dataset from an alternative campus of the primary hospital, used to validate clinical probing-based periodontitis diagnosis (Phase 1) and gingival inflammation monitoring (Phase 3).
External Validation Dataset II (Two Regional Hospitals): Separate imaging datasets from two distinct regional medical centers, used to validate radiograph-based periodontitis diagnosis (Phase 2). Receives: Diagnostic Test: AI-driven Periodontal Diagnostic and Monitoring Algorithms |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Accuracy of the AI Model for Probing-Based Periodontitis Staging
The diagnostic performance of the deep learning AI model in classifying periodontitis stages (binary classification: Stage 0/I vs. Stage II/III/IV) at both individual tooth and patient levels, using comprehensive clinical periodontal probing as the gold standard. Performance will be evaluated using the internal development dataset and verified using External Validation Dataset I (secondary campus data). Metrics will include Area Under the Receiver Operating Characteristic curve (AUC), Sensitivity, Specificity, and F1-score.
Measured: Baseline (At a single point in time for each participant (cross-sectional assessment))
Diagnostic Accuracy of the AI Model for Radiograph-Based Periodontitis Staging
The diagnostic performance of the deep learning AI model in classifying periodontitis stages (binary classification: Stage 0/I vs. Stage II/III/IV) at both individual tooth and patient levels, using digital panoramic radiographs as the reference standard. Performance will be evaluated using the internal development dataset and verified using External Validation Dataset II (multi-center data from two separate regional hospitals). Metrics will include Area Under the Receiver Operating Characteristic curve (AUC), Sensitivity, Specificity, and F1-score.
Measured: Baseline (At a single point in time for each participant (cross-sectional assessment))
Diagnostic Accuracy of the AI Model for Gingival Inflammation Monitoring
The performance of the deep learning AI model in detecting and monitoring gingival inflammation flags at both individual tooth and patient levels, using Probing Depth (PD) and Bleeding on Probing (BOP) metrics as the clinical gold standard. Performance will be evaluated using the internal development dataset and verified using External Validation Dataset I (secondary campus data). Metrics will include Sensitivity, Specificity, Positive Predictive Value (PPV), and Negative Predictive Value (NPV).
Measured: Baseline (At a single point in time for each participant (cross-sectional assessment))
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Jun 2026
- Study start10 Sep 2025
- Primary completion10 Mar 2026
- Study completion10 Sep 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Periodontal DiseaseStudies 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
- Cross-sectional
- Enrolment
- 900 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Junyu Shi, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07629934
- Sponsor's protocol ID
- SH9H-2025-T196-4
- Status last verified
- Jun 2026
- First submitted
- 1 Jun 2026
- Last updated
- 5 Jun 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 5 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of Artificial Intelligence Models for Periodontitis Diagnosis and Gingival Inflammation Monitoring at Tooth and Patient Levels: A Diagnostic Accuracy Study
- ClinicalTrials.gov ID
- NCT07629934
- Acronym / sponsor ID
- SH9H-2025-T196-4
- Conditions
- Periodontal Disease
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 900 participants (estimated)
- Lead sponsor
- Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong UniversityUniversity, hospital or other
- Study start
- 10 Sep 2025 (actual)
- Primary completion
- 10 Mar 2026 (actual)
- Study completion
- 10 Sep 2026 (estimated)
- First posted
- 5 Jun 2026 (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.
Background and Objective:
Periodontitis and gingivitis are highly prevalent oral diseases that require accurate diagnostic classification and continuous gingival health monitoring. This study aims to develop, internally validate, and externally evaluate the diagnostic accuracy of artificial intelligence (AI) models for periodontitis staging and gingival inflammation assessment at both tooth and patient levels.
Study Design:
This is a multi-center observational study utilizing a large-scale primary clinical dataset for model development. To rigorously evaluate the generalizability of the trained AI models, two distinct pathways of independent external validation will be implemented across multiple clinical sites.
Research Phases \& Validation Architecture:
Phase 1 (Periodontitis Diagnosis via Probing): Development of an AI model to diagnose periodontitis (binary classification: stage 0/I vs. stage II/III/IV) at both tooth and patient levels, using comprehensive clinical periodontal probing as the gold standard. External Validation I will be performed using an independent cohort from another campus of the primary hospital to test the model's diagnostic accuracy.
Phase 2 (Periodontitis Diagnosis via Radiographs): Development of an AI model to diagnose periodontitis (binary classification: stage 0/I vs. stage II/III/IV) at both tooth and patient levels, using digital panoramic radiographs as the reference standard. External Validation II will be conducted using distinct, independent image datasets acquired from two separate regional hospitals to evaluate geographic generalizability.
Phase 3 (Gingival Inflammation Monitoring): Development of an AI model to monitor and assess gingival inflammation at both tooth and patient levels, based on Probing Depth (PD) and Bleeding on Probing (BOP) as the gold standard. This model's performance will also be evaluated through External Validation I using the independent dataset from the primary hospital's alternative campus.
Significance:
By validating the AI models across varied institutional workflows and imaging systems, this study will provide high-level evidence on the clinical utility and robustness of AI-driven digital systems for automated periodontal screening and long-term health monitoring.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Multi-center Periodontal AI Development and Validation Cohort | – | Diagnostic Test: AI-driven Periodontal Diagnostic and Monitoring AlgorithmsDevelopment Dataset (Primary Campus): Large-scale data used for the initial training and internal validation of the AI algorithms. External Validation Dataset I (Secondary Campus): An independent dataset from an alternative campus of the primary hospital, used to validate clinical probing-based periodontitis diagnosis (Phase 1) and gingival inflammation monitoring (Phase 3). External Validation Dataset II (Two Regional Hospitals): Separate imaging datasets from two distinct regional medical centers, used to validate radiograph-based periodontitis diagnosis (Phase 2). |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of adult patients who sought routine dental care or periodontal evaluation at the primary medical center (including its main campus and an alternative secondary campus) and two independent regional hospitals. This multi-center population reflects a real-world, diverse clinical screening pool of patients presenting with varying degrees of periodontal health, ranging from completely healthy gingiva to severe, advanced periodontitis. Eligible participants are identified based on the availability of concurrent full-mouth clinical periodontal charting and digital panoramic radiographs.
Key inclusion criteria (3 listed)
- Patients aged > 18 years at the time of their clinical periodontal examination.
- Availability of complete full-mouth periodontal charting records, which must include Probing Depth (PD) and Bleeding on Probing (BOP) documented at 6 sites per tooth.
- Availability of a digital panoramic radiograph of acceptable diagnostic quality, taken within one months of the clinical periodontal examination.
Key exclusion criteria (4 listed)
- Patients who are completely edentulous or those who have undergone full-arch dental implant rehabilitation (not applicable for natural teeth periodontitis staging).
- Panoramic radiographs with severe image degradation, including major motion artifacts, severe positioning errors, or poor contrast/exposure that obscures the alveolar bone crest.
- Presence of extensive metal artifacts or massive bilateral multiple fixed crowns/bridges that completely shadow the marginal bone level of interest.
- Incomplete clinical electronic medical records or missing core diagnostic descriptors required to establish the clinical gold standard for periodontitis staging or gingival inflammation.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-driven Periodontal Diagnostic and Monitoring AlgorithmsThe intervention evaluated in this observational study is the deployment of deep learning/artificial intelligence (AI) software models. The AI algorithms process two streams of standard clinical data to perform three automated diagnostic tasks without altering patient care: Automated classification of periodontitis stages (Stage 0/I vs. Stage II/III/IV) utilizing full-mouth clinical charting metrics. Automated classification of periodontitis stages (Stage 0/I vs. Stage II/III/IV) utilizing digital panoramic radiographs. Automated assessment and monitoring of gingival inflammation flags based on Probing Depth (PD) and Bleeding on Probing (BOP) patterns. The outputs of these AI models will be directly compared against clinical and radiographic gold standards to calculate diagnostic accuracy metrics. | Diagnostic test | Multi-center Periodontal AI Development and Validation Cohort |
Primary outcome
Diagnostic Accuracy of the AI Model for Probing-Based Periodontitis Staging
Time frame: Baseline (At a single point in time for each participant (cross-sectional assessment))
The diagnostic performance of the deep learning AI model in classifying periodontitis stages (binary classification: Stage 0/I vs. Stage II/III/IV) at both individual tooth and patient levels, using comprehensive clinical periodontal probing as the gold standard. Performance will be evaluated using the internal development dataset and verified using External Validation Dataset I (secondary campus data). Metrics will include Area Under the Receiver Operating Characteristic curve (AUC), Sensitivity, Specificity, and F1-score.
Diagnostic Accuracy of the AI Model for Radiograph-Based Periodontitis Staging
Time frame: Baseline (At a single point in time for each participant (cross-sectional assessment))
The diagnostic performance of the deep learning AI model in classifying periodontitis stages (binary classification: Stage 0/I vs. Stage II/III/IV) at both individual tooth and patient levels, using digital panoramic radiographs as the reference standard. Performance will be evaluated using the internal development dataset and verified using External Validation Dataset II (multi-center data from two separate regional hospitals). Metrics will include Area Under the Receiver Operating Characteristic curve (AUC), Sensitivity, Specificity, and F1-score.
Diagnostic Accuracy of the AI Model for Gingival Inflammation Monitoring
Time frame: Baseline (At a single point in time for each participant (cross-sectional assessment))
The performance of the deep learning AI model in detecting and monitoring gingival inflammation flags at both individual tooth and patient levels, using Probing Depth (PD) and Bleeding on Probing (BOP) metrics as the clinical gold standard. Performance will be evaluated using the internal development dataset and verified using External Validation Dataset I (secondary campus data). Metrics will include Sensitivity, Specificity, Positive Predictive Value (PPV), and Negative Predictive Value (NPV).
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 5 Jun 2026.
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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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
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 Disease, 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: under way, not looking for participants, as of the sponsor's last update on 5 Jun 2026. 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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Accuracy of the AI Model for Probing-Based Periodontitis Staging (time frame: Baseline (At a single point in time for each participant (cross-sectional assessment))). The study lists 3 primary outcomes in all. 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 5 Jun 2026; AzTrials last refreshed its copy on 7 Nov 2026.