Development and Validation of the Periodontal Map Derived From IOS and CBCT Registration for Diagnosis and Treatment Planning in Moderate-to-severe Periodontitis

NCT07505251 | Not Yet Recruiting

• 2 other identifiers: SH9H-2026-T98-1JYLJ2025015
• Study Type: observational
• Target: 80
• Locations: 0 countries
• Sites: N/A

Brief 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 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.

Conditions

Periodontitis Stage II; Periodontitis Stage III; Periodontitis Stage IV

Outcome Measures

Primary Outcomes (1)

• 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.

  Preoperative (PerioAI system analysis) and intraoperative (direct surgical observation)

Secondary Outcomes (6)

• Agreement Between PerioAI-Automated Probing Depth Measurements and Clinical Probing Depth

  Preoperative (PerioAI system analysis and clinical examination)

• Agreement Between PerioAI-Automated Clinical Attachment Loss Measurements and Clinical Attachment Loss

  Preoperative (PerioAI system analysis and clinical examination)

• Agreement Between PerioAI-Automated Periodontitis Staging and Grading and Clinical Staging and Grading

  Preoperative (PerioAI system analysis and clinical examination)

• Accuracy of Furcation Involvement Grading by PerioAI System Compared to Intraoperative Findings

  Preoperative (PerioAI system analysis) and intraoperative (direct surgical exploration)

• Agreement Between PerioAI-Automated Intrabony Defect Depth Measurements and Intraoperative Direct Measurements

  Preoperative (PerioAI system analysis) and intraoperative (direct surgical measurement)

Study Arms (1)

Periodontitis Surgical Cohort

Diagnostic Test: Perio AI V2.0 System

Interventions

Perio AI V2.0 System DIAGNOSTIC_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.

Periodontitis Surgical Cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patient diagnosed with Stage II-IV periodontitis and has at least one tooth requiring periodontal surgery

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University: lead

Related Publications (1)

• 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.

  PMID: 40532658 BACKGROUND

Biospecimen

Retention: SAMPLES WITH DNA

oral rinse and saliva

MeSH Terms

Conditions

Periodontitis

Condition Hierarchy (Ancestors)

Periodontal Diseases; Mouth Diseases; Stomatognathic Diseases

Central Study Contacts

Yuan Li, Dr.

CONTACT

13916337473; ly9919@hotmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Day
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 18, 2026
• First Posted: April 1, 2026
• Study Start: April 1, 2026
• Primary Completion (Estimated): April 30, 2028
• Study Completion (Estimated): December 31, 2028
• Last Updated: April 1, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share