A Multicenter Clinical Study to Validate the Performance Improvement of Fundus Photography Reading Software
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 10
- 试验地点
- 5
- 主要终点
- Performance of readers with and without AI assistance: Sensitivity
研究概览
简要总结
The purpose of this multi-center study is to evaluate the extent to which AI-assisted fundus image interpretation improves the diagnostic performance of ophthalmologists. Rather than assessing the standalone algorithm performance, this study aims to determine the clinical value of using AI as a decision-support tool within actual clinical workflows.
At each participating institution, five ophthalmologists within three years of board certification and five ophthalmology residents will participate as readers. All readers will interpret fundus images both with and without the AI-based assistance software. The study will quantitatively compare diagnostic accuracy and reading time across the two conditions for four posterior segment diseases: diabetic retinopathy, age-related macular degeneration, retinal vein occlusion, and glaucoma.
研究设计
- 研究类型
- Interventional
- 分配方式
- Non Randomized
- 干预模型
- Sequential
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Licensed physicians qualified to interpret fundus images.
- •Ophthalmologists within three years of board certification, or ophthalmology residents with no restriction on clinical experience.
- •Able and willing to complete both the unassisted and AI-assisted reading sessions.
- •Able to provide informed consent for participation in the reader study.
- •Affiliated with one of the participating clinical sites.
排除标准
- 未提供
研究组 & 干预措施
AI-Assisted Reading
Readers interpret the fundus images with AI-generated outputs available.
干预措施: VUNO Med-Fundus AI (Device)
Unassisted Reading
Readers interpret fundus images without access to the AI system.
结局指标
主要结局
Performance of readers with and without AI assistance: Sensitivity
时间窗: Through study completion, approximately 2 months
Sensitivity of reader diagnoses for each of the four target diseases (DR, AMD, RVO, glaucoma) and for any fundus abnormality will be assessed with and without AI assistance, using the image-level reference standard as the comparator, through two reading sessions in which all 10 readers review all cases-randomised for each reader-with a washout period implemented to mitigate recall bias.
Performance of readers with and without AI assistance: Specificity
时间窗: Through study completion, approximately 2 months
Specificity of reader diagnoses for each of the four target diseases (DR, AMD, RVO, glaucoma) and for any fundus abnormality will be assessed with and without AI assistance, using the image-level reference standard as the comparator, through two reading sessions in which all 10 readers review all cases-randomised for each reader-with a washout period implemented to mitigate recall bias.
Reading time per image
时间窗: Through study completion, approximately 2 months
Reading time per image will be measured during both unassisted and AI-assisted interpretation sessions. For each case, the total time from the moment the image is displayed to the moment the reader submits the final disease classification will be recorded automatically by the reading platform. Mean reading time per image will be calculated for each reader and compared between the two conditions to evaluate whether AI assistance reduces interpretation time.
次要结局
未报告次要终点
研究者
Dong Geun Kim
Assistant Professor of Ophthalmology
Inje University