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Artificial Intelligence for Diagnosing Periodontitis and Monitoring Gingival Inflammation

4 juni 2026 bijgewerkt door: Junyu Shi, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University

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.

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.

Studie Overzicht

Toestand

Actief, niet wervend

Conditie

Interventie / Behandeling

Studietype

Observationeel

Inschrijving (Geschat)

900

Contacten en locaties

In dit gedeelte vindt u de contactgegevens van degenen die het onderzoek uitvoeren en informatie over waar dit onderzoek wordt uitgevoerd.

Studie Locaties

  • China
      • Shanghai, China
        • Shanghai Ninth People's Hospital affiliated to Shanghai Jiao Tong University School of Medicine

Deelname Criteria

Onderzoekers zoeken naar mensen die aan een bepaalde beschrijving voldoen, de zogenaamde geschiktheidscriteria. Enkele voorbeelden van deze criteria zijn iemands algemene gezondheidstoestand of eerdere behandelingen.

Geschiktheidscriteria

Leeftijden die in aanmerking komen voor studie

  • Volwassen
  • Oudere volwassene

Accepteert gezonde vrijwilligers

Ja

Bemonsteringsmethode

Niet-waarschijnlijkheidssteekproef

Studie Bevolking

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.

Beschrijving

Inclusion Criteria:

  1. Patients aged > 18 years at the time of their clinical periodontal examination.
  2. 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.
  3. Availability of a digital panoramic radiograph of acceptable diagnostic quality, taken within one months of the clinical periodontal examination.

Exclusion Criteria:

  1. Patients who are completely edentulous or those who have undergone full-arch dental implant rehabilitation (not applicable for natural teeth periodontitis staging).
  2. Panoramic radiographs with severe image degradation, including major motion artifacts, severe positioning errors, or poor contrast/exposure that obscures the alveolar bone crest.
  3. Presence of extensive metal artifacts or massive bilateral multiple fixed crowns/bridges that completely shadow the marginal bone level of interest.
  4. Incomplete clinical electronic medical records or missing core diagnostic descriptors required to establish the clinical gold standard for periodontitis staging or gingival inflammation.

Studie plan

Dit gedeelte bevat details van het studieplan, inclusief hoe de studie is opgezet en wat de studie meet.

Hoe is de studie opgezet?

Ontwerpdetails

Cohorten en interventies

Groep / Cohort
Interventie / Behandeling
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).
Diagnostische toets: AI-driven Periodontal Diagnostic and Monitoring Algorithms

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.

Wat meet het onderzoek?

Primaire uitkomstmaten

Uitkomstmaat
Maatregel Beschrijving
Tijdsspanne
Diagnostic Accuracy of the AI Model for Probing-Based Periodontitis Staging
Tijdsspanne: 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.
Baseline (At a single point in time for each participant (cross-sectional assessment))
Diagnostic Accuracy of the AI Model for Radiograph-Based Periodontitis Staging
Tijdsspanne: 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.
Baseline (At a single point in time for each participant (cross-sectional assessment))
Diagnostic Accuracy of the AI Model for Gingival Inflammation Monitoring
Tijdsspanne: 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).
Baseline (At a single point in time for each participant (cross-sectional assessment))

Medewerkers en onderzoekers

Hier vindt u mensen en organisaties die betrokken zijn bij dit onderzoek.

Sponsor

Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University

Studie record data

Deze datums volgen de voortgang van het onderzoeksdossier en de samenvatting van de ingediende resultaten bij ClinicalTrials.gov. Studieverslagen en gerapporteerde resultaten worden beoordeeld door de National Library of Medicine (NLM) om er zeker van te zijn dat ze voldoen aan specifieke kwaliteitscontrolenormen voordat ze op de openbare website worden geplaatst.

Bestudeer belangrijke data

Studie start (Werkelijk)

10 september 2025

Primaire voltooiing (Werkelijk)

10 maart 2026

Studie voltooiing (Geschat)

10 september 2026

Studieregistratiedata

Eerst ingediend

1 juni 2026

Eerst ingediend dat voldeed aan de QC-criteria

4 juni 2026

Eerst geplaatst (Werkelijk)

5 juni 2026

Updates van studierecords

Laatste update geplaatst (Werkelijk)

5 juni 2026

Laatste update ingediend die voldeed aan QC-criteria

4 juni 2026

Laatst geverifieerd

1 juni 2026

Meer informatie

Termen gerelateerd aan deze studie

Aanvullende relevante MeSH-voorwaarden

Andere studie-ID-nummers

  • SH9H-2025-T196-4

Informatie over medicijnen en apparaten, studiedocumenten

Bestudeert een door de Amerikaanse FDA gereguleerd geneesmiddel

Nee

Bestudeert een door de Amerikaanse FDA gereguleerd apparaatproduct

Nee

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