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.
Systemically and periodontally healthy individuals.
Presence of natural maxillary anterior incisors.
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.
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.
Other
Cross-sectional
Periodontal Probe Transparency Method
Diagnostic testStandardized intraoral photography of the maxillary anterior teeth with a periodontal probe placed according to the transparency method protocol to determine probe visibility status.
Visual Assessment Method
Diagnostic testStandardized intraoral photography of the maxillary anterior teeth without a periodontal probe, evaluated for gingival phenotype classification based on morphological features.
Deep Learning-Based Artificial Intelligence Model
Other interventionA deep learning image classification algorithm trained to assess probe visibility and gingival phenotype from standardized intraoral photographs.
Diagnostic Accuracy of Each Examiner Group and AI Model in the Periodontal Probe Transparency Method
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.
Diagnostic Accuracy of Each Examiner Group and AI Model in Visual Assessment Method
Accuracy in classifying gingival phenotype (thin vs. thick) without probe, compared to the gold standard classification. Measure Type: Proportion (%).
Agreement Between Examiner Groups and AI Model
Inter-examiner and intra-examiner agreement for each method, evaluated using Cohen's kappa coefficient and intraclass correlation coefficient (ICC).
Effect of Examiner Experience Level on Diagnostic Performance
Comparison of accuracy and agreement between research assistants and dental intern students for each method. Proportion (%), agreement statistic.
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