Artificial Intelligence-Assisted Lesion-Based Urgent Referral Triage of Ultra-Widefield Retinal Images (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.
研究概览
地位
条件
详细说明
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.
研究类型
注册 (估计的)
阶段
- 不适用
联系人和位置
学习联系方式
- 姓名：Xiuju Chen, md
- 电话号码：+8618060955810
- 邮箱：joyychen@aliyun.com
参与标准
资格标准
适合学习的年龄
- 成人
- 年长者
接受健康志愿者
描述
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
学习计划
研究是如何设计的？
设计细节
- 主要用途：诊断
- 分配：随机化
- 介入模型：阶乘赋值
- 屏蔽：无（打开标签）
武器和干预
参与者组/臂 |
干预/治疗 |
|---|---|
|
实验性的：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.
|
|
有源比较器：Unassisted Interpretation
Readers interpret UWF retinal images without AI assistance.
|
Readers interpret UWF retinal images without AI assistance.
|
研究衡量的是什么？
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
|
Correct Lesion-Based Urgent Referral Triage Rate
大体时间：Through study completion, up to 2 months
|
Proportion of reader referral decisions consistent with expert-adjudicated lesion-based urgent referral classifications.
|
Through study completion, up to 2 months
|
次要结果测量
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
|
Reader Confidence Score
大体时间：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.
|
|
Sensitivity for Urgent Referral Findings
大体时间：Through study completion, up to 2 months
|
Sensitivity for correctly classifying non-urgent referral images according to expert-adjudicated lesion-based triage labels.
|
Through study completion, up to 2 months
|
|
Specificity for Urgent Referral Findings
大体时间：Through study completion, up to 2 months
|
Specificity for correctly classifying non-urgent referral images according to expert-adjudicated lesion-based triage labels.
|
Through study completion, up to 2 months
|
|
False-Negative Rate for Urgent Referral Findings
大体时间：Through study completion, up to 2 months
|
Proportion of urgent referral images incorrectly classified as non-urgent referral by readers.
|
Through study completion, up to 2 months
|
|
False-Positive Rate for Urgent Referral Findings
大体时间：Through study completion, up to 2 months
|
Proportion of non-urgent referral images incorrectly classified as urgent referral by readers.
|
Through study completion, up to 2 months
|
|
Change in Correct Urgent Referral Decisions After AI Assistance
大体时间：Through study completion, up to 2 months
|
Number and proportion of cases in which AI assistance changed an incorrect referral decision to a correct referral decision.
|
Through study completion, up to 2 months
|
合作者和调查者
研究记录日期
研究主要日期
学习开始 (估计的)
初级完成 (估计的)
研究完成 (估计的)
研究注册日期
首次提交
首先提交符合 QC 标准的
首次发布 (实际的)
研究记录更新
最后更新发布 (实际的)
上次提交的符合 QC 标准的更新
最后验证
更多信息
与本研究相关的术语
关键字
其他研究编号
- XMYKZX-KY-2026-011
计划个人参与者数据 (IPD)
计划共享个人参与者数据 (IPD)？
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