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
地位
条件
条件
干预/治疗
干预/治疗
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
研究类型
注册 (估计的)
注册
阶段
阶段
- 不适用
联系人和位置
学习联系方式
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
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)
手臂数量
武器和干预
参与者组/臂参与者组/臂 |
干预/治疗干预/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
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
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)
学习开始
初级完成 (估计的)
初级完成
研究完成 (估计的)
研究完成
研究注册日期
首次提交
首次提交
首先提交符合 QC 标准的
首先提交符合 QC 标准的
首次发布 (实际的)
首次发布
研究记录更新
最后更新发布 (实际的)
最后更新发布
上次提交的符合 QC 标准的更新
上次提交的符合 QC 标准的更新
最后验证
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
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)?
药物和器械信息、研究文件
研究美国 FDA 监管的药品
研究美国 FDA 监管的设备产品
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