Periodontitis Stage IINCT07505251
Development and Validation of the Periodontal Map Derived From IOS and CBCT Registration for Diagnosis and Treatment Planning in Moderate-to-severe Periodontitis
An observational study of Periodontitis Stage II and Periodontitis Stage III, run by Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University.
Trial summary
This prospective diagnostic study aims to validate the clinical utility of a "Periodontal Panoramic Map" generated by the PerioAI V2.0 system, an artificial intelligence-based platform that integrates intraoral scans and cone-beam CT data, for preoperative diagnosis and surgical planning in patients with moderate to… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Periodontitis Stage II, Periodontitis Stage III, Periodontitis Stage IV
- What is being tested?
- Perio AI V2.0 System
- Type of treatment
- Pill or oral medicine
- How many people
- 80 participants
- Where
- No sites listed yet
- Study dates
- 1 Apr 2026to 31 Dec 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
- No healthy volunteers
- Adult, Older adult
Study population: Patient diagnosed with Stage II-IV periodontitis and has at least one tooth requiring periodontal surgery
Who may be able to join
- Aged ≥ 18 years.
- Diagnosed with Stage II-IV periodontitis according to the 2018 Classification of Periodontal Diseases.
- Presence of at least one tooth requiring periodontal surgery (including open flap debridement or regenerative surgery) due to periodontitis, where the intra-bony defect can be exposed intra-operatively for measurement.
- Voluntary participation and provision of written informed consent.
Who cannot join
- Pregnant or lactating women.
- Presence of uncontrolled systemic diseases that significantly affect surgery or tissue healing, such as uncontrolled diabetes mellitus or immunodeficiency.
- History of head and neck radiotherapy.
- Inability to cooperate with the required study examinations.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
This prospective diagnostic study aims to validate the clinical utility of a "Periodontal Panoramic Map" generated by the PerioAI V2.0 system, an artificial intelligence-based platform that integrates intraoral scans and cone-beam CT data, for preoperative diagnosis and surgical planning in patients with moderate to severe periodontitis (Stage II-IV). Current clinical standards-manual probing and two-dimensional radiography-have inherent limitations in accurately visualizing complex three-dimensional bone defect morphology, leading to potential underestimation of disease severity and suboptimal surgical outcomes. Building upon our team's previously published high-precision PerioAI V1.0 system, this study will enroll 80 patients requiring periodontal surgery. Preoperative intraoral scans and cone-beam CT images will be acquired as part of routine care, and the PerioAI V2.0 system will automatically generate a "Periodontal Panoramic Map" with intelligent outputs including probing depth, clinical attachment loss, bone defect morphology classification, furcation involvement grading, and automated measurements of key parameters such as intra-bony defect depth and width. These automated diagnostic results will be compared against the gold standard of full mouth clinical examination and intra-operative direct measurements and observations obtained during periodontal surgery under strict blinded conditions. The primary outcome measures are the accuracy of bone defect morphology classification and the agreement between automated and intra-operative linear measurements assessed by intraclass correlation coefficients and Bland-Altman analysis. Secondary outcomes include accuracy of probing depth, clinical attachment loss, periodontitis staging and grading, furcation involvement grading and treatment planning. This study will provide critical evidence supporting the paradigm shift in periodontal surgery from experience-dependent assessment to data-driven precision medicine, ultimately offering clinicians an intuitive, quantitative, and three-dimensional visualization tool for optimized surgical decision-making.
- Being tested
- Perio AI V2.0 System
- Medical subject headings
- Periodontitis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Perio AI V2.0 SystemDiagnostic test
This is a single-arm, prospective diagnostic accuracy study. The intervention is the application of an artificial intelligence-based software (PerioAI V2.0) to routinely acquired preoperative intra-oral scan and cone-beam CT data. The software generates a "Periodontal Panoramic Map" with automated measurements and classifications. All participants then undergo routine full clinical examination and clinically indicated periodontal surgery, which are obtained as the gold standard to validate the accuracy of the Perio AI V2.0 system's preoperative diagnostic outputs. The study does not involve any experimental therapeutic interventions; all surgical procedures are part of standard care.
| Study group | Role | What they receive |
|---|---|---|
| Periodontitis Surgical Cohort | Not described Receives: Diagnostic Test: Perio AI V2.0 System |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of Bone Defect Morphology Classification by PerioAI System Compared to Intraoperative Findings
The PerioAI 2.0 system automatically classifies bone defect morphology (1-wall, 2-wall, 3-wall intrabony defects, dehiscence, or fenestration) based on preoperative intraoral scan and cone-beam CT data. The classification accuracy is assessed by comparing the PerioAI-generated classification against the gold standard of intraoperative direct visual observation by an experienced surgeon during periodontal surgery. Results are reported as the percentage of correctly classified defects (accuracy rate), with sensitivity and specificity for each defect type.
Measured: Preoperative (PerioAI system analysis) and intraoperative (direct surgical observation)
Secondary outcomes (6)
Agreement Between PerioAI-Automated Probing Depth Measurements and Clinical Probing Depth
Probing depth (PD) is measured in millimeters at six sites per tooth (mesio-buccal, mid-buccal, disto-buccal; mesio-lingual, mid-lingual, disto-lingual). The PerioAI system automatically measures PD from fused intraoral scan and cone-beam CT data. These automated measurements are compared with clinical PD measurements obtained by a trained periodontist using a manual periodontal probe (UNC-15). Agreement is assessed using intraclass correlation coefficients and Bland-Altman analysis.
Measured: Preoperative (PerioAI system analysis and clinical examination)
Agreement Between PerioAI-Automated Clinical Attachment Loss Measurements and Clinical Attachment Loss
Clinical attachment loss (CAL) is measured in millimeters as the distance from the cementoenamel junction to the base of the pocket at six sites per tooth. The PerioAI system automatically measures CAL from fused intraoral scan and cone-beam CT data. These automated measurements are compared with clinical CAL measurements obtained by a trained periodontist using a manual periodontal probe (UNC-15). Agreement is assessed using intraclass correlation coefficients and Bland-Altman analysis.
Measured: Preoperative (PerioAI system analysis and clinical examination)
Agreement Between PerioAI-Automated Periodontitis Staging and Grading and Clinical Staging and Grading
Periodontitis stage (II, III, or IV) and grade (A, B, or C) are determined according to the 2018 Classification of Periodontal Diseases. The PerioAI system automatically assigns stage and grade based on analysis of intraoral scan and cone-beam CT data (including radiographic bone loss, tooth loss, defect complexity, and other parameters). These automated classifications are compared with clinical staging and grading performed by a trained periodontist using full-mouth periodontal charting, radiographic review, and the 2018 classification criteria. Agreement is assessed using weighted kappa statistics.
Measured: Preoperative (PerioAI system analysis and clinical examination)
Accuracy of Furcation Involvement Grading by PerioAI System Compared to Intraoperative Findings
Furcation involvement is graded as Grade I, II, or III according to the 2018 Classification of Periodontal Diseases. The PerioAI system automatically assigns furcation involvement grade based on cone-beam CT data. The accuracy of the PerioAI-generated grading is assessed by comparing it against the gold standard of intraoperative direct exploration using a Nabers probe during periodontal surgery. Results are reported as the percentage of correctly graded furcations (accuracy rate), with sensitivity and specificity for each grade.
Measured: Preoperative (PerioAI system analysis) and intraoperative (direct surgical exploration)
Agreement Between PerioAI-Automated Intrabony Defect Depth Measurements and Intraoperative Direct Measurements
Intrabony defect depth (INTRA) is measured in millimeters from the alveolar crest to the base of the defect. The PerioAI system automatically measures INTRA from fused intraoral scan and cone-beam CT data. These automated measurements are compared with direct intraoperative measurements obtained by an experienced surgeon using a periodontal probe after flap elevation. Agreement is assessed using intraclass correlation coefficients and Bland-Altman analysis.
Measured: Preoperative (PerioAI system analysis) and intraoperative (direct surgical measurement)
Agreement Between PerioAI-Automated Intrabony Defect Width Measurements and Intraoperative Direct Measurements
Intrabony defect width (WIDTH) is measured in millimeters from the root surface to the most coronal part of the bony defect. The PerioAI system automatically measures WIDTH from fused intraoral scan and cone-beam CT data. These automated measurements are compared with direct intraoperative measurements obtained by an experienced surgeon using a periodontal probe after flap elevation. Agreement is assessed using intraclass correlation coefficients and Bland-Altman analysis.
Measured: Preoperative (PerioAI system analysis) and intraoperative (direct surgical measurement)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Apr 2026
- Study start1 Apr 2026 (estimated)
- Primary completion30 Apr 2028 (estimated)
- Study completion31 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Periodontitis Stage IIStudies 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
- 80 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07505251
- Sponsor's protocol ID
- SH9H-2026-T98-1
- Other IDs
- JYLJ2025015
- Status last verified
- Mar 2026
- First submitted
- 18 Mar 2026
- Last updated
- 1 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Tan M, Cui Z, Li Y, Fang Y, Mei L, Zhao Y, Wu X, Lai H, Tonetti MS, Shen D. PerioAI: A digital system for periodontal disease diagnosis from an intra-oral scan and cone-beam CT image. Cell Rep Med. 2025 Jun 17;6(6):102186. doi: 10.1016/j.xcrm.2025.102186.
PubMed 40532658
Study at a glance
From the registry record, last updated by the sponsor on 1 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of the Periodontal Map Derived From IOS and CBCT Registration for Diagnosis and Treatment Planning in Moderate-to-severe Periodontitis
- ClinicalTrials.gov ID
- NCT07505251
- Acronym / sponsor ID
- SH9H-2026-T98-1
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 80 participants (estimated)
- Lead sponsor
- Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong UniversityUniversity, hospital or other
- Study start
- 1 Apr 2026 (estimated)
- Primary completion
- 30 Apr 2028 (estimated)
- Study completion
- 31 Dec 2028 (estimated)
- First posted
- 1 Apr 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.
This prospective diagnostic study aims to validate the clinical utility of a "Periodontal Panoramic Map" generated by the PerioAI V2.0 system, an artificial intelligence-based platform that integrates intraoral scans and cone-beam CT data, for preoperative diagnosis and surgical planning in patients with moderate to severe periodontitis (Stage II-IV). Current clinical standards-manual probing and two-dimensional radiography-have inherent limitations in accurately visualizing complex three-dimensional bone defect morphology, leading to potential underestimation of disease severity and suboptimal surgical outcomes. Building upon our team's previously published high-precision PerioAI V1.0 system, this study will enroll 80 patients requiring periodontal surgery. Preoperative intraoral scans and cone-beam CT images will be acquired as part of routine care, and the PerioAI V2.0 system will automatically generate a "Periodontal Panoramic Map" with intelligent outputs including probing depth, clinical attachment loss, bone defect morphology classification, furcation involvement grading, and automated measurements of key parameters such as intra-bony defect depth and width. These automated diagnostic results will be compared against the gold standard of full mouth clinical examination and intra-operative direct measurements and observations obtained during periodontal surgery under strict blinded conditions. The primary outcome measures are the accuracy of bone defect morphology classification and the agreement between automated and intra-operative linear measurements assessed by intraclass correlation coefficients and Bland-Altman analysis. Secondary outcomes include accuracy of probing depth, clinical attachment loss, periodontitis staging and grading, furcation involvement grading and treatment planning. This study will provide critical evidence supporting the paradigm shift in periodontal surgery from experience-dependent assessment to data-driven precision medicine, ultimately offering clinicians an intuitive, quantitative, and three-dimensional visualization tool for optimized surgical decision-making.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Day
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Periodontitis Surgical Cohort | – | Diagnostic Test: Perio AI V2.0 System |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patient diagnosed with Stage II-IV periodontitis and has at least one tooth requiring periodontal surgery
Key inclusion criteria (4 listed)
- Aged ≥ 18 years.
- Diagnosed with Stage II-IV periodontitis according to the 2018 Classification of Periodontal Diseases.
- Presence of at least one tooth requiring periodontal surgery (including open flap debridement or regenerative surgery) due to periodontitis, where the intra-bony defect can be exposed intra-operatively for measurement.
- Voluntary participation and provision of written informed consent.
Key exclusion criteria (4 listed)
- Pregnant or lactating women.
- Presence of uncontrolled systemic diseases that significantly affect surgery or tissue healing, such as uncontrolled diabetes mellitus or immunodeficiency.
- History of head and neck radiotherapy.
- Inability to cooperate with the required study examinations.
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 |
|---|---|---|
| Perio AI V2.0 SystemThis is a single-arm, prospective diagnostic accuracy study. The intervention is the application of an artificial intelligence-based software (PerioAI V2.0) to routinely acquired preoperative intra-oral scan and cone-beam CT data. The software generates a "Periodontal Panoramic Map" with automated measurements and classifications. All participants then undergo routine full clinical examination and clinically indicated periodontal surgery, which are obtained as the gold standard to validate the accuracy of the Perio AI V2.0 system's preoperative diagnostic outputs. The study does not involve any experimental therapeutic interventions; all surgical procedures are part of standard care. | Diagnostic test | Periodontitis Surgical Cohort |
Primary outcome
Accuracy of Bone Defect Morphology Classification by PerioAI System Compared to Intraoperative Findings
Time frame: Preoperative (PerioAI system analysis) and intraoperative (direct surgical observation)
The PerioAI 2.0 system automatically classifies bone defect morphology (1-wall, 2-wall, 3-wall intrabony defects, dehiscence, or fenestration) based on preoperative intraoral scan and cone-beam CT data. The classification accuracy is assessed by comparing the PerioAI-generated classification against the gold standard of intraoperative direct visual observation by an experienced surgeon during periodontal surgery. Results are reported as the percentage of correctly classified defects (accuracy rate), with sensitivity and specificity for each defect type.
6 secondary outcomes
Agreement Between PerioAI-Automated Probing Depth Measurements and Clinical Probing Depth
Time frame: Preoperative (PerioAI system analysis and clinical examination)
Agreement Between PerioAI-Automated Clinical Attachment Loss Measurements and Clinical Attachment Loss
Time frame: Preoperative (PerioAI system analysis and clinical examination)
Agreement Between PerioAI-Automated Periodontitis Staging and Grading and Clinical Staging and Grading
Time frame: Preoperative (PerioAI system analysis and clinical examination)
Accuracy of Furcation Involvement Grading by PerioAI System Compared to Intraoperative Findings
Time frame: Preoperative (PerioAI system analysis) and intraoperative (direct surgical exploration)
Agreement Between PerioAI-Automated Intrabony Defect Depth Measurements and Intraoperative Direct Measurements
Time frame: Preoperative (PerioAI system analysis) and intraoperative (direct surgical measurement)
Agreement Between PerioAI-Automated Intrabony Defect Width Measurements and Intraoperative Direct Measurements
Time frame: Preoperative (PerioAI system analysis) and intraoperative (direct surgical measurement)
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 1 Apr 2026.
- Background: PubMed 40532658Tan M, Cui Z, Li Y, Fang Y, Mei L, Zhao Y, Wu X, Lai H, Tonetti MS, Shen D. PerioAI: A digital system for periodontal disease diagnosis from an intra-oral scan and cone-beam CT image. Cell Rep Med. 2025 Jun 17;6(6):102186. doi: 10.1016/j.xcrm.2025.102186.
- Individual participant dataSharing planned: No
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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.
Questions about this trial
What is this clinical trial studying?
An observational study of Periodontitis Stage II and Periodontitis Stage III, run by Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University.
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 4 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 1 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
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 Yuan Li, Dr. (13916337473, ly9919@hotmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of Bone Defect Morphology Classification by PerioAI System Compared to Intraoperative Findings (time frame: Preoperative (PerioAI system analysis) and intraoperative (direct surgical observation)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07505251, which also lists 1 linked publication.
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 1 Apr 2026.