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.
調査の概要
研究の種類
入学 (推定)
連絡先と場所
研究場所
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Shanghai、中国
- Shanghai Ninth People's Hospital affiliated to Shanghai Jiao Tong University School of Medicine
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参加基準
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
サンプリング方法
調査対象母集団
説明
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.
研究計画
研究はどのように設計されていますか？
デザインの詳細
コホートと介入
グループ/コホート |
介入・治療 |
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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. |
この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
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Diagnostic Accuracy of the AI Model for Probing-Based Periodontitis Staging
時間枠：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
時間枠：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
時間枠：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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協力者と研究者
研究記録日
主要日程の研究
研究開始 (実際)
一次修了 (実際)
研究の完了 (推定)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
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