Comparison of Artificial Intelligence and Clinicians With Different Experience Levels in Assessing Gingival Phenotype
The goal of this observational study is to compare the performance of clinicians with different experience levels and a deep learning-based artificial intelligence (AI) model in assessing gingival phenotype using two diagnostic methods: the periodontal probe transparency method and visual assessment from standardized clinical photographs. The main questions the study aims to answer are:
Can AI achieve comparable accuracy to human examiners in both probe transparency and visual assessment methods?
Does examiner experience level influence diagnostic performance and agreement with the reference standard in these methods?
Researchers will compare AI, dental students, and periodontology research assistants to determine accuracy, sensitivity, specificity, and agreement with the gold standard for each method.
Participants will:
Undergo standardized intraoral photography of maxillary anterior teeth, with and without a periodontal probe in place, following a validated protocol.
Have gingival phenotype determined by a reference periodontologist using the probe transparency method as the gold standard.
Have their photographs evaluated by AI, dental students, and research assistants for phenotype classification using both methods.
調査の概要
状態
詳細な説明
Gingival phenotype, representing the thickness and morphological characteristics of the gingival soft tissues, plays a critical role in periodontal health, treatment planning, and the long-term stability of clinical outcomes. A thin phenotype is associated with increased risk of gingival recession, papilla loss, and inflammatory complications, while a thick phenotype offers better soft tissue stability but may mask inflammation. Accurate and reproducible assessment of gingival phenotype is therefore essential in clinical dentistry.
The periodontal probe transparency method is considered the gold standard for phenotype assessment due to its simplicity and non-invasiveness. In this method, a periodontal probe is inserted into the sulcus from the buccal aspect, and if the probe is visible through the gingival tissue, the phenotype is classified as thin; if not visible, it is classified as thick. However, the method is susceptible to variability depending on examiner experience, lighting conditions, and subjective interpretation.
Visual assessment, which relies solely on the inspection of gingival and tooth morphology in photographs without a probe, offers a non-contact alternative but is similarly subject to examiner-related variability. These limitations highlight the need for standardized and objective approaches to phenotype determination.
Artificial intelligence (AI), particularly deep learning-based image analysis, has shown promising results in dental diagnostics, enabling automated classification of clinical images with high accuracy and reproducibility. In periodontal research, AI has been applied for lesion detection and radiographic interpretation, but its application in gingival phenotype assessment-especially using the probe transparency method and visual assessment-remains unexplored.
This observational study aims to compare the diagnostic performance of a deep learning-based AI model with human examiners of different experience levels (periodontology residents vs. dental students) in assessing gingival phenotype from standardized intraoral photographs using both the periodontal probe transparency method and visual assessment. The reference standard will be the classification provided by an experienced periodontologist using the probe transparency method in a clinical setting.
The study will evaluate and compare accuracy, sensitivity, specificity, and inter-/intra-examiner agreement across examiner groups and the AI model. The findings are expected to provide insights into the potential of AI as a standardizing tool, reducing inter-examiner variability and supporting clinical decision-making, particularly for less experienced clinicians. Additionally, the study may inform the integration of AI-assisted diagnostic tools in dental education and practice, improving training efficiency and clinical outcomes.
研究の種類
入学 (推定)
連絡先と場所
研究連絡先
- 名前：Sude Yıldırım Bolat, DDS
- 電話番号：+905378947645
- メール：sugde.sude@gmail.com
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
The study population will consist of systemically and periodontally healthy adults attending the Department of Periodontology at Ondokuz Mayıs University, Faculty of Dentistry, for routine dental care or check-up. Eligible participants will have natural maxillary anterior incisors and meet all inclusion criteria.
Additionally, the examiner population will include:
Periodontology research assistants currently working in the department.
Fourth- and fifth-year dental intern students who have completed the periodontology clinical rotation.
説明
Inclusion Criteria for Volunteer Participants Who Will Participate in Transparency and Visual Assessment:
- Systemically and periodontally healthy individuals.
- Presence of natural maxillary anterior incisors.
Exclusion Criteria:
- Presence of fixed crowns or cervical restorations on the evaluated teeth.
- Pregnant or breastfeeding women.
- Signs of gingival inflammation or periodontal disease with attachment loss.
- Presence of buccal gingival recession.
- Use of medications known to cause gingival enlargement.
- Presence of congenital anomalies or dental structural defects.
Inclusion Criteria for Clinicians:
- Research assistants: Must be currently working in the Department of Periodontology.
- Dental Intern Students: Fourth- or fifth-year students who have completed periodontology clinical rotation.
Exclusion Criteria for Clinicians:
- Those who are confirmed to be color blind by the Ishihara test
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 |
|---|---|
|
Dental Students
Fourth- and fifth-year dental intern students will assess standardized intraoral photographs using both the periodontal probe transparency method and visual assessment to classify gingival phenotype.
|
Standardized intraoral photography of the maxillary anterior teeth with a periodontal probe placed according to the transparency method protocol to determine probe visibility status.
Standardized intraoral photography of the maxillary anterior teeth without a periodontal probe, evaluated for gingival phenotype classification based on morphological features.
|
|
Artificial Intelligence Model
A deep learning-based image classification model will analyze standardized intraoral photographs, detecting probe visibility and classifying gingival phenotype according to the periodontal probe transparency method and visual assessment criteria.
|
Standardized intraoral photography of the maxillary anterior teeth with a periodontal probe placed according to the transparency method protocol to determine probe visibility status.
Standardized intraoral photography of the maxillary anterior teeth without a periodontal probe, evaluated for gingival phenotype classification based on morphological features.
A deep learning image classification algorithm trained to assess probe visibility and gingival phenotype from standardized intraoral photographs.
|
|
Periodontology Research Assistants
Research assistants in periodontology will assess standardized intraoral photographs using both the periodontal probe transparency method and visual assessment to classify gingival phenotype.
|
Standardized intraoral photography of the maxillary anterior teeth with a periodontal probe placed according to the transparency method protocol to determine probe visibility status.
Standardized intraoral photography of the maxillary anterior teeth without a periodontal probe, evaluated for gingival phenotype classification based on morphological features.
|
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 |
|---|---|---|
|
Diagnostic Accuracy of Each Examiner Group and AI Model in the Periodontal Probe Transparency Method
時間枠：At the time of image evaluation (single session).
|
Accuracy in determining probe visibility (visible vs. not visible) compared to the gold standard classification by an experienced periodontologist. Measure Type: Proportion (%). Analysis: Accuracy, sensitivity, specificity, and Cohen's kappa coefficient will be calculated. |
At the time of image evaluation (single session).
|
二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Diagnostic Accuracy of Each Examiner Group and AI Model in Visual Assessment Method
時間枠：At the time of image evaluation (single session).
|
Accuracy in classifying gingival phenotype (thin vs. thick) without probe, compared to the gold standard classification. Measure Type: Proportion (%). |
At the time of image evaluation (single session).
|
|
Agreement Between Examiner Groups and AI Model
時間枠：At the time of image evaluation and at 2-week retest (for a random subset of evaluators).
|
Inter-examiner and intra-examiner agreement for each method, evaluated using Cohen's kappa coefficient and intraclass correlation coefficient (ICC).
|
At the time of image evaluation and at 2-week retest (for a random subset of evaluators).
|
|
Effect of Examiner Experience Level on Diagnostic Performance
時間枠：At the time of image evaluation (single session).
|
Comparison of accuracy and agreement between research assistants and dental intern students for each method. Proportion (%), agreement statistic. |
At the time of image evaluation (single session).
|
協力者と研究者
スポンサー
出版物と役立つリンク
研究記録日
主要日程の研究
研究開始 (推定)
一次修了 (推定)
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
本研究に関する用語
キーワード
その他の研究ID番号
- OMUKAEK NO:225/335
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
IPD プランの説明
IPD 共有時間枠
IPD 共有アクセス基準
IPD 共有サポート情報タイプ
- STUDY_PROTOCOL
- SAP
- ICF
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
米国FDA規制機器製品の研究
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