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
연구 개요
상세 설명
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.
연구 유형
등록 (실제)
연락처 및 위치
연구 장소
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Cairo Governorate
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Cairo, Cairo Governorate, 이집트, 11754
- Faculty of Dental Medicine for Girls, Al-Azhar University
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참여기준
자격 기준
공부할 수 있는 나이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
샘플링 방법
연구 인구
설명
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.
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
코호트 및 개입
그룹/코호트 |
개입 / 치료 |
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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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연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
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Accuracy of Artificial Intelligence-Based Keratinized Gingiva Width Measurement
기간: 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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공동 작업자 및 조사자
연구 기록 날짜
연구 주요 날짜
연구 시작 (실제)
기본 완료 (실제)
연구 완료 (실제)
연구 등록 날짜
최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
추가 정보
이 연구와 관련된 용어
키워드
기타 연구 ID 번호
- OMPDR 108-1q
개별 참가자 데이터(IPD) 계획
개별 참가자 데이터(IPD)를 공유할 계획입니까?
IPD 계획 설명
약물 및 장치 정보, 연구 문서
미국 FDA 규제 의약품 연구
미국 FDA 규제 기기 제품 연구
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치주질환에 대한 임상 시험
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University of Pennsylvania완전한Intrntl Classification of Diseases, 9th Revision, (ICD-9-CM) 410의 주진단 또는 이차진단 코드가 있는 환자(5번째 숫자가 2인 경우 제외)미국