Development and Validation of the Periodontal Map Derived From IOS and CBCT Registration for Diagnosis and Treatment Planning in Moderate-to-severe Periodontitis
Trial ID: NCT07505251
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04-01
Completion date: 2028-12-31
Sponsor: Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University
Conditions: Periodontitis Stage II; Periodontitis Stage III; Periodontitis Stage IV
Interventions: Perio AI V2.0 System
Enrollment: 80
Age range: 18 Years - None
Sex: ALL

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.

Eligibility criteria:
Inclusion Criteria:

1. Aged ≥ 18 years.
2. Diagnosed with Stage II-IV periodontitis according to the 2018 Classification of Periodontal Diseases.
3. 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.
4. Voluntary participation and provision of written informed consent.

Exclusion Criteria:

1. Pregnant or lactating women.
2. Presence of uncontrolled systemic diseases that significantly affect surgery or tissue healing, such as uncontrolled diabetes mellitus or immunodeficiency.
3. History of head and neck radiotherapy.
4. Inability to cooperate with the required study examinations.

Outcomes:
Accuracy of Bone Defect Morphology Classification by PerioAI System Compared to Intraoperative Findings