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Artificial Intelligence for Diagnosing Periodontitis and Monitoring Gingival Inflammation

4. juni 2026 opdateret af: 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.

Studieoversigt

Status

Aktiv, ikke rekrutterende

Betingelser

Intervention / Behandling

Undersøgelsestype

Observationel

Tilmelding (Anslået)

900

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiesteder

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

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

  • Voksen
  • Ældre voksen

Tager imod sunde frivillige

Ja

Prøveudtagningsmetode

Ikke-sandsynlighedsprøve

Studiebefolkning

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.

Beskrivelse

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.

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

Kohorter og interventioner

Gruppe / kohorte
Intervention / Behandling
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).
Diagnostisk test: 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.

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Diagnostic Accuracy of the AI Model for Probing-Based Periodontitis Staging
Tidsramme: 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
Tidsramme: 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
Tidsramme: 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))

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

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

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Faktiske)

10. september 2025

Primær færdiggørelse (Faktiske)

10. marts 2026

Studieafslutning (Anslået)

10. september 2026

Datoer for studieregistrering

Først indsendt

1. juni 2026

Først indsendt, der opfyldte QC-kriterier

4. juni 2026

Først opslået (Faktiske)

5. juni 2026

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

5. juni 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

4. juni 2026

Sidst verificeret

1. juni 2026

Mere information

Begreber relateret til denne undersøgelse

Yderligere relevante MeSH-vilkår

Andre undersøgelses-id-numre

  • SH9H-2025-T196-4

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