- ICH GCP
- 미국 임상 시험 레지스트리
- 임상시험 NCT07908017
Diagnostic Performance of Ai vs Junior Dentists
Comparison of Artificial Intelligence vs. Junior Dentists Diagnostic Performance for Radiographic Caries & Periapical Lesion Detection
OBJECTIVES
- Compare diagnosis of Periapical lesions & Proximal caries by AI and junior dentists
- Evaluate Validity of a deep learning based convolutional neural network (CNN) for detecting proximal caries lesions, periapical lesions compared to junior dentists.
연구 개요
상태
정황
상세 설명
Statement of problem Current evidence supports the use of AI as a valuable diagnostic platform adjunct for detecting periapical lesions. However, well-designed, high-quality randomized clinical trials are required to assess the potential implementation of AI in the routine practice of periapical lesion diagnosis. In addition, most studies were done on OPG and CBCT not on periapical radiographs. Evidence suggests that AI-assisted systems are effective in detecting large, distinct periapical lesions. However, their performance drops in more complex cases, such as with root-filled teeth and in small periodical lesions The performance of AI in caries detection remains moderate, and its periapical status assessment is insufficient due to high false-positive rates
Research Questions
- Does Trained AI outperform junior dentists in basic diagnostic tasks, specifically in detecting tooth caries and periapical lesions through radiographic interpretation?
- Does AI increase the accuracy of lesion detection?
- Is AI more accurate than junior dentists in detecting carious lesions or periapical lesions?
- Is there a difference between diagnosis of Caries vs Periapical lesions through AI tools compared to junior dentists?
연구 유형
등록 (추정된)
연락처 및 위치
연구 연락처
- 이름: Susan Hattar, PhD
- 전화번호: +962795642613
- 이메일: s.hattar@ju.edu.jo
연구 장소
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Amman, 요르단, 11181
- The university of Jordan
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참여기준
자격 기준
공부할 수 있는 나이
건강한 자원 봉사자를 받아들입니다
연구 대상 성별
샘플링 방법
연구 인구
Patients over 18 years of age that were clear and displayed the full anatomical crown and root apex.
설명
Inclusion Criteria:
- Bitewings obtained from patients treated at Jordan University Hospital. Only bitewings of permanent maxillary and mandibular teeth, with the crowns of both dental arch being detectable. Coronal part of the tooth had to be in toto visible on the radiograph and that no structure is projected on the examined tooth, teeth with overlap free projection selected
- Periapical Radiographs: patients over 18 years of age that were clear and displayed the full anatomical crown and root apex.
Exclusion Criteria:
- Radiographs of deciduous teeth or mixed dentition, those with severe noise or haziness, radiographs of partially edentulous areas, teeth exhibiting abnormal crown or root anatomy, and any radiographs that were difficult to assess.
- Overlapping images, radiographs with distortions, noise and shadows, and low quality radiographs
- Radiographs excluded if at least 2-3 examiners did not agree.
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
- 관찰 모델: COHORT
- 시간 관점: CROSS_SECTIONAL
연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
|
(ROCAUC)
기간: 12 months
|
Receiver-Operating-Characteristics curve |
12 months
|
2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Diagnostic test parameters
기간: 12 months
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Sensitivity, Specificity, PPV, NPV |
12 months
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공동 작업자 및 조사자
수사관
- 연구 책임자: Susan Hattar, PhD, University of Jordan
간행물 및 유용한 링크
일반 간행물
- Mertens S, Krois J, Cantu AG, Arsiwala LT, Schwendicke F. Artificial intelligence for caries detection: Randomized trial. J Dent. 2021 Dec;115:103849. doi: 10.1016/j.jdent.2021.103849. Epub 2021 Oct 14.
- Schwendicke F, Tzschoppe M, Paris S. Radiographic caries detection: A systematic review and meta-analysis. J Dent. 2015 Aug;43(8):924-33. doi: 10.1016/j.jdent.2015.02.009. Epub 2015 Feb 24.
- Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi JG, Gaudin R, Elhennawy K, Schwendicke F. Detecting caries lesions of different radiographic extension on bitewings using deep learning. J Dent. 2020 Sep;100:103425. doi: 10.1016/j.jdent.2020.103425. Epub 2020 Jul 4.
- Putra RH, Doi C, Yoda N, Astuti ER, Sasaki K. Current applications and development of artificial intelligence for digital dental radiography. Dentomaxillofac Radiol. 2022 Jan 1;51(1):20210197. doi: 10.1259/dmfr.20210197. Epub 2021 Jul 8.
- Kuhnisch J, Meyer O, Hesenius M, Hickel R, Gruhn V. Caries Detection on Intraoral Images Using Artificial Intelligence. J Dent Res. 2022 Feb;101(2):158-165. doi: 10.1177/00220345211032524. Epub 2021 Aug 20.
연구 기록 날짜
연구 주요 날짜
연구 시작 (추정된)
기본 완료 (추정된)
연구 완료 (추정된)
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
추가 관련 MeSH 약관
기타 연구 ID 번호
- ToothVision
- IRB (기타 식별자: IRB Jordan University Hospital)
개별 참가자 데이터(IPD) 계획
개별 참가자 데이터(IPD)를 공유할 계획입니까?
약물 및 장치 정보, 연구 문서
미국 FDA 규제 의약품 연구
미국 FDA 규제 기기 제품 연구
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