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

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 Overview

• Status: Not yet recruiting
• Conditions: Periodontitis Stage II; Periodontitis Stage III; Periodontitis Stage IV
• Intervention / Treatment: Diagnostic test: Perio AI V2.0 System

• Study Type: Observational
• Enrollment (Estimated): 80

Contacts and Locations

• Study Contact: Name: Yuan Li, Dr.; Phone Number: 13916337473; Email: ly9919@hotmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

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

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

Periodontitis Surgical Cohort

Diagnostic test: Perio AI V2.0 System; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	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.	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.	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.	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.	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.	Preoperative (PerioAI system analysis) and intraoperative (direct surgical measurement)

Collaborators and Investigators

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

Publications and helpful links

General 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.

Study record dates

Study Major Dates

• Study Start (Estimated): April 1, 2026
• Primary Completion (Estimated): April 30, 2028
• Study Completion (Estimated): December 31, 2028

Study Registration Dates

• First Submitted: March 18, 2026
• First Submitted That Met QC Criteria: March 26, 2026
• First Posted (Actual): April 1, 2026

Study Record Updates

• Last Update Posted (Actual): April 1, 2026
• Last Update Submitted That Met QC Criteria: March 26, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Periodontal Diseases; Mouth Diseases; Stomatognathic Diseases; Periodontitis
• Other Study ID Numbers: SH9H-2026-T98-1; JYLJ2025015 (Other Grant/Funding Number: Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No