NCT06211218招募中不适用
Application of Deep Learning for Screening Multiple Corneal Diseases
适应症
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 3,000
- 试验地点
- 1
- 主要终点
- Area under curve
研究概览
简要总结
This study developed a deep learning algorithm based on anterior segment images and prospectively validated its ability to identify corneal diseases.The effectiveness and accuracy of this algorithm was evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and area under curve.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Prospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •The quality of slit-lamp images should clinical acceptable.
- •More than 90% of the slit-lamp image area including three main regions (sclera, pupil, and lens) are easy to read and discriminate.
排除标准
- •1）Insufficient information for diagnosis.
结局指标
主要结局
Area under curve
时间窗: 1 week
We used the receiver operating characteristic (ROC) curve and area under curve to examine the ability of this artificial intelligence algorism recognition and classification of corneal diseases.
Sensitivity and specificity
时间窗: 1 week
We used sensitivity and specificity to examine the ability of this artificial intelligence algorism recognition and classification of corneal diseases.
次要结局
未报告次要终点
研究者
研究点 (1)
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