Artificial Intelligence-Assisted Lesion-Based Urgent Referral Triage of Ultra-Widefield Retinal Images: A Multi-Reader Multi-Case Randomized Reader Study (ALERT-UWF)
Clinical Utility of an Artificial Intelligence-Assisted Lesion-Based Urgent Referral Triage System for Ultra-Widefield Retinal Images: A Prospective Multi-Reader Multi-Case Randomized Reader Study
his study evaluates the clinical utility of an artificial intelligence (AI)-assisted lesion-based urgent referral triage system for ultra-widefield (UWF) retinal images.
Unlike disease-classification systems, the AI system identifies predefined vision-threatening retinal findings and generates lesion-level urgent referral recommendations. Participating ophthalmologists will evaluate UWF retinal images under randomized AI-assisted and unassisted conditions.
The primary objective is to determine whether lesion-based AI assistance improves urgent referral triage performance compared with unaided image interpretation.
연구 개요
상태
정황
상세 설명
Ultra-widefield retinal imaging is increasingly used for retinal disease screening and referral triage. Many vision-threatening retinal abnormalities require timely identification and referral to retinal specialists.
The AI system evaluated in this study is designed as a lesion-based triage tool rather than a disease-diagnosis system. The model identifies predefined urgent referral retinal findings and generates referral recommendations based on lesion-level evidence.
Urgent referral findings include:
- Retinal detachment
- Untreated retinal tear or retinal hole
- Vitreous hemorrhage
- Pre-retinal hemorrhage
- Subretinal hemorrhage
- Retinal neovascularization
- Optic disc neovascularization
- Tractional fibrovascular membrane Treated retinal tears associated with laser barricade scars are classified as non-urgent referral findings.
A total of 600 UWF retinal images acquired using Zeiss and Optos imaging systems will be included.
Participating ophthalmologists will independently evaluate images in randomized AI-assisted and unassisted settings.
The primary objective is to determine whether AI assistance improves lesion-based urgent referral triage accuracy.
연구 유형
등록 (추정된)
단계
- 해당 없음
연락처 및 위치
연구 연락처
- 이름: Xiuju Chen, md
- 전화번호: +8618060955810
- 이메일: joyychen@aliyun.com
참여기준
자격 기준
공부할 수 있는 나이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
설명
Inclusion Criteria:
- Licensed ophthalmologists
- Willing to participate as readers
- Completion of study training
Exclusion Criteria:
- Retinal specialists involved in establishing gold-standard labels
- Prior access to gold-standard labels
- Incomplete study participation
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
- 주 목적: 특수 증상
- 할당: 무작위
- 중재 모델: 요인 할당
- 마스킹: 없음(오픈 라벨)
무기와 개입
참가자 그룹 / 팔 |
개입 / 치료 |
|---|---|
|
실험적: AI-Assisted Interpretation
Readers interpret UWF retinal images with lesion-level AI findings and urgent referral recommendations.
|
Readers interpret UWF retinal images with lesion-level AI findings and urgent referral recommendations.
|
|
활성 비교기: Unassisted Interpretation
Readers interpret UWF retinal images without AI assistance.
|
Readers interpret UWF retinal images without AI assistance.
|
연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
|
Correct Lesion-Based Urgent Referral Triage Rate
기간: Immediately after image interpretation.
|
Proportion of reader referral decisions consistent with expert-adjudicated lesion-based urgent referral classifications.
|
Immediately after image interpretation.
|
2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
|
Sensitivity for Urgent Referral Findings
기간: Immediately after image interpretation.
|
Sensitivity for Urgent Referral Findings
|
Immediately after image interpretation.
|
|
Specificity for Urgent Referral Findings
기간: Immediately after image interpretation.
|
Specificity for correctly classifying non-urgent referral images according to expert-adjudicated lesion-based triage labels.
|
Immediately after image interpretation.
|
|
False-Negative Rate for Urgent Referral Findings
기간: Immediately after image interpretation.
|
Proportion of urgent referral images incorrectly classified as non-urgent referral by readers.
|
Immediately after image interpretation.
|
|
False-Positive Rate for Urgent Referral Findings
기간: Immediately after image interpretation.
|
Proportion of non-urgent referral images incorrectly classified as urgent referral by readers.
|
Immediately after image interpretation.
|
|
Reader Confidence Score
기간: Immediately after image interpretation.
|
Reader-reported confidence level for referral decisions measured using a 5-point Likert scale, ranging from 1 (very uncertain) to 5 (very confident).
|
Immediately after image interpretation.
|
|
Change in Correct Urgent Referral Decisions After AI Assistance
기간: Immediately after image interpretation.
|
Number and proportion of cases in which AI assistance changed an incorrect referral decision to a correct referral decision.
|
Immediately after image interpretation.
|
공동 작업자 및 조사자
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
키워드
추가 관련 MeSH 약관
기타 연구 ID 번호
- XMYKZX-KY-2026-011
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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