Artificial Intelligence for Diagnosing Periodontitis and Monitoring Gingival Inflammation
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.
연구 개요
연구 유형
등록 (추정된)
연락처 및 위치
연구 장소
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Shanghai, 중국
- Shanghai Ninth People's Hospital affiliated to Shanghai Jiao Tong University School of Medicine
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참여기준
자격 기준
공부할 수 있는 나이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
샘플링 방법
연구 인구
설명
Inclusion Criteria:
- Patients aged > 18 years at the time of their clinical periodontal examination.
- 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.
- Availability of a digital panoramic radiograph of acceptable diagnostic quality, taken within one months of the clinical periodontal examination.
Exclusion Criteria:
- Patients who are completely edentulous or those who have undergone full-arch dental implant rehabilitation (not applicable for natural teeth periodontitis staging).
- Panoramic radiographs with severe image degradation, including major motion artifacts, severe positioning errors, or poor contrast/exposure that obscures the alveolar bone crest.
- Presence of extensive metal artifacts or massive bilateral multiple fixed crowns/bridges that completely shadow the marginal bone level of interest.
- Incomplete clinical electronic medical records or missing core diagnostic descriptors required to establish the clinical gold standard for periodontitis staging or gingival inflammation.
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
코호트 및 개입
그룹/코호트 |
개입 / 치료 |
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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). |
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. |
연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
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Diagnostic Accuracy of the AI Model for Probing-Based Periodontitis Staging
기간: Baseline (At a single point in time for each participant (cross-sectional assessment))
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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.
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Baseline (At a single point in time for each participant (cross-sectional assessment))
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Diagnostic Accuracy of the AI Model for Radiograph-Based Periodontitis Staging
기간: Baseline (At a single point in time for each participant (cross-sectional assessment))
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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.
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Baseline (At a single point in time for each participant (cross-sectional assessment))
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Diagnostic Accuracy of the AI Model for Gingival Inflammation Monitoring
기간: Baseline (At a single point in time for each participant (cross-sectional assessment))
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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).
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Baseline (At a single point in time for each participant (cross-sectional assessment))
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공동 작업자 및 조사자
연구 기록 날짜
연구 주요 날짜
연구 시작 (실제)
기본 완료 (실제)
연구 완료 (추정된)
연구 등록 날짜
최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
추가 정보
이 정보는 변경 없이 clinicaltrials.gov 웹사이트에서 직접 가져온 것입니다. 귀하의 연구 세부 정보를 변경, 제거 또는 업데이트하도록 요청하는 경우 register@clinicaltrials.gov. 문의하십시오. 변경 사항이 clinicaltrials.gov에 구현되는 즉시 저희 웹사이트에도 자동으로 업데이트됩니다. .
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