Deep Learning-Based Measurement of Keratinized Gingiva Width Using Smartphone-Acquired Clinical Images
A Deep Learning-Based Analytical Framework for Detection, Quantification, and Quality Assessment of Keratinized Gingival Tissues in Clinical Examination Images
Studienübersicht
Status
Bedingungen
Intervention / Behandlung
Detaillierte Beschreibung
This observational diagnostic validation study was conducted to develop and evaluate an artificial intelligence-based system for automated assessment of keratinized gingiva width (KGW) using smartphone-acquired intraoral clinical photographs.
Standardized intraoral images were collected from eligible participants following predefined inclusion and exclusion criteria. All images were captured using a smartphone under standardized clinical conditions to ensure uniformity in lighting, angulation, and image quality. Clinical measurements of keratinized gingiva width were independently performed by two calibrated expert examiners, serving as the reference (ground truth) standard.
A deep learning-based model was trained to segment and measure the keratinized gingival tissue from clinical images. The predicted measurements generated by the AI system were compared against the expert clinical measurements to evaluate model performance.
The performance of the system was assessed using multiple evaluation metrics, including accuracy, Dice similarity coefficient, Intersection over Union (IoU), precision, recall, and F1-score. Inter-examiner reliability between experts was also considered to ensure consistency of the reference standard.
The study aims to demonstrate the feasibility of integrating artificial intelligence into periodontal diagnostics, specifically for objective and reproducible measurement of keratinized gingiva width. The proposed system may contribute to reducing inter-operator variability and improving clinical efficiency in periodontal assessment.
Studientyp
Einschreibung (Tatsächlich)
Kontakte und Standorte
Studienorte
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Cairo Governorate
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Cairo, Cairo Governorate, Ägypten, 11754
- Faculty of Dental Medicine for Girls, Al-Azhar University
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Teilnahmekriterien
Zulassungskriterien
Studienberechtigtes Alter
- Erwachsene
- Älterer Erwachsener
Akzeptiert gesunde Freiwillige
Probenahmeverfahren
Studienpopulation
Beschreibung
Inclusion Criteria:
- Patients aged 18 years or older.
Patients with varying periodontal conditions thealthy. gingivitis, periodontitie.
Patients willing to provide adormed consent.
Exclusion Criteria:
- Patients with a history of periodontal surgery within the past six montie
Patients withsystemic conditions affecting oraltissue eg. diabetes.
Very poor quality intra oral image.
Studienplan
Wie ist die Studie aufgebaut?
Designdetails
Kohorten und Interventionen
Gruppe / Kohorte |
Intervention / Behandlung |
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Participants Undergoing Keratinized Gingiva Assessment
Participants whose smartphone-acquired intraoral clinical photographs were used for assessment of keratinized gingiva width.
Clinical measurements performed by expert examiners served as the reference standard for validation of the artificial intelligence model.
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Analysis of smartphone-acquired intraoral photographs using a deep learning model for automated measurement of keratinized gingiva width.
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Was misst die Studie?
Primäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
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Accuracy of Artificial Intelligence-Based Keratinized Gingiva Width Measurement
Zeitfenster: Baseline (single study visit)
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Evaluation of the agreement between keratinized gingiva width measurements generated by the artificial intelligence model and reference measurements obtained by calibrated examiners using smartphone-acquired intraoral clinical photographs at the baseline clinical visit.
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Baseline (single study visit)
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Mitarbeiter und Ermittler
Sponsor
Studienaufzeichnungsdaten
Haupttermine studieren
Studienbeginn (Tatsächlich)
Primärer Abschluss (Tatsächlich)
Studienabschluss (Tatsächlich)
Studienanmeldedaten
Zuerst eingereicht
Zuerst eingereicht, das die QC-Kriterien erfüllt hat
Zuerst gepostet (Tatsächlich)
Studienaufzeichnungsaktualisierungen
Letztes Update gepostet (Tatsächlich)
Letztes eingereichtes Update, das die QC-Kriterien erfüllt
Zuletzt verifiziert
Mehr Informationen
Begriffe im Zusammenhang mit dieser Studie
Schlüsselwörter
Zusätzliche relevante MeSH-Bedingungen
Andere Studien-ID-Nummern
- OMPDR 108-1q
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