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Artificial Intelligence for Diagnosing Periodontitis and Monitoring Gingival Inflammation

4 de junho de 2026 atualizado por: 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.

Visão geral do estudo

Status

Ativo, não recrutando

Condições

Intervenção / Tratamento

Tipo de estudo

Observacional

Inscrição (Estimado)

900

Contactos e Locais

Esta seção fornece os detalhes de contato para aqueles que conduzem o estudo e informações sobre onde este estudo está sendo realizado.

Locais de estudo

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

Critérios de participação

Os pesquisadores procuram pessoas que se encaixem em uma determinada descrição, chamada de critérios de elegibilidade. Alguns exemplos desses critérios são a condição geral de saúde de uma pessoa ou tratamentos anteriores.

Critérios de elegibilidade

Idades elegíveis para estudo

  • Adulto
  • Adulto mais velho

Aceita Voluntários Saudáveis

Sim

Método de amostragem

Amostra Não Probabilística

População do estudo

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.

Descrição

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.

Plano de estudo

Esta seção fornece detalhes do plano de estudo, incluindo como o estudo é projetado e o que o estudo está medindo.

Como o estudo é projetado?

Detalhes do projeto

Coortes e Intervenções

Grupo / Coorte
Intervenção / Tratamento
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).
Teste de diagnostico: 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.

O que o estudo está medindo?

Medidas de resultados primários

Medida de resultado
Descrição da medida
Prazo
Diagnostic Accuracy of the AI Model for Probing-Based Periodontitis Staging
Prazo: 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
Prazo: 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
Prazo: 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))

Colaboradores e Investigadores

É aqui que você encontrará pessoas e organizações envolvidas com este estudo.

Patrocinador

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

Datas de registro do estudo

Essas datas acompanham o progresso do registro do estudo e os envios de resumo dos resultados para ClinicalTrials.gov. Os registros do estudo e os resultados relatados são revisados ​​pela National Library of Medicine (NLM) para garantir que atendam aos padrões específicos de controle de qualidade antes de serem publicados no site público.

Datas Principais do Estudo

Início do estudo (Real)

10 de setembro de 2025

Conclusão Primária (Real)

10 de março de 2026

Conclusão do estudo (Estimado)

10 de setembro de 2026

Datas de inscrição no estudo

Enviado pela primeira vez

1 de junho de 2026

Enviado pela primeira vez que atendeu aos critérios de CQ

4 de junho de 2026

Primeira postagem (Real)

5 de junho de 2026

Atualizações de registro de estudo

Última Atualização Postada (Real)

5 de junho de 2026

Última atualização enviada que atendeu aos critérios de controle de qualidade

4 de junho de 2026

Última verificação

1 de junho de 2026

Mais Informações

Termos relacionados a este estudo

Termos MeSH relevantes adicionais

Outros números de identificação do estudo

  • SH9H-2025-T196-4

Informações sobre medicamentos e dispositivos, documentos de estudo

Estuda um medicamento regulamentado pela FDA dos EUA

Não

Estuda um produto de dispositivo regulamentado pela FDA dos EUA

Não

Essas informações foram obtidas diretamente do site clinicaltrials.gov sem nenhuma alteração. Se você tiver alguma solicitação para alterar, remover ou atualizar os detalhes do seu estudo, entre em contato com register@clinicaltrials.gov. Assim que uma alteração for implementada em clinicaltrials.gov, ela também será atualizada automaticamente em nosso site .

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