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Deep Learning-Based Measurement of Keratinized Gingiva Width Using Smartphone-Acquired Clinical Images

7. juli 2026 opdateret af: Salma Lamloum Mohamed Mohamed, Al-Azhar University

A Deep Learning-Based Analytical Framework for Detection, Quantification, and Quality Assessment of Keratinized Gingival Tissues in Clinical Examination Images

This study aims to develop and validate an artificial intelligence-based system for automated measurement of keratinized gingiva width using smartphone-acquired intraoral clinical photographs. Standardized intraoral images will be collected and analyzed using a deep learning model, and the results will be compared with clinical measurements performed by calibrated expert examiners, which serve as the reference standard. The performance of the proposed system will be evaluated using accuracy metrics including Dice coefficient, Intersection over Union (IoU), precision, recall, and F1-score. This study seeks to support the integration of AI tools into periodontal diagnosis and clinical decision-making to improve measurement consistency and reduce inter-examiner variability.

Studieoversigt

Status

Afsluttet

Betingelser

Intervention / Behandling

Detaljeret beskrivelse

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.

Undersøgelsestype

Observationel

Tilmelding (Faktiske)

50

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiesteder

  • Egypten
    • Cairo Governorate
      • Cairo, Cairo Governorate, Egypten, 11754
        • Faculty of Dental Medicine for Girls, Al-Azhar University

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

  • Voksen
  • Ældre voksen

Tager imod sunde frivillige

Ja

Prøveudtagningsmetode

Ikke-sandsynlighedsprøve

Studiebefolkning

Participants attending the clinic of the department of Periodontologly Faculty of Dental Medicine for girls Al-Azhar university who met the study eligibility criteria and provided smartphone-acquired intraoral clinical photographs for keratinized gingiva width assessment and artificial intelligence model validation.

Beskrivelse

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.

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

Kohorter og interventioner

Gruppe / kohorte
Intervention / Behandling
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.
Diagnostisk test: Artificial Intelligence-Based Keratinized Gingiva Width Assessment
Analysis of smartphone-acquired intraoral photographs using a deep learning model for automated measurement of keratinized gingiva width.

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Accuracy of Artificial Intelligence-Based Keratinized Gingiva Width Measurement
Tidsramme: Baseline (single study visit)
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.
Baseline (single study visit)

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

Al-Azhar University

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Faktiske)

1. juli 2025

Primær færdiggørelse (Faktiske)

9. januar 2026

Studieafslutning (Faktiske)

15. marts 2026

Datoer for studieregistrering

Først indsendt

30. juni 2026

Først indsendt, der opfyldte QC-kriterier

7. juli 2026

Først opslået (Faktiske)

8. juli 2026

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

8. juli 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

7. juli 2026

Sidst verificeret

1. juli 2026

Mere information

Begreber relateret til denne undersøgelse

Nøgleord

Yderligere relevante MeSH-vilkår

Andre undersøgelses-id-numre

  • OMPDR 108-1q

Plan for individuelle deltagerdata (IPD)

Planlægger du at dele individuelle deltagerdata (IPD)?

INGEN

IPD-planbeskrivelse

IPD will not be shared to protect patient confidentiality and in compliance with institutional ethical guidelines. Data access is limited to the study investigators only.

Lægemiddel- og udstyrsoplysninger, undersøgelsesdokumenter

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Ingen

Studerer et amerikansk FDA-reguleret enhedsprodukt

Ingen

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Kliniske forsøg med Periodontale sygdomme

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Sponsorer og samarbejdspartnere

Medicinske tilstande

Narkotikainterventioner

CROs by country

CROs in Kuwait

Betingelser

Sjældne sygdomme

Narkotikainterventioner

Kosttilskud

Sponsor / samarbejdspartnere

Placeringer

3
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