NCT04213183已完成不适用
Screening and Identifying Hepatobiliary Diseases Via Deep Learning Using Ocular Images
Sun Yat-sen University1 个研究点 分布在 1 个国家目标入组 1,789 人开始时间: 2018年12月1日最近更新:
适应症
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 1,789
- 试验地点
- 1
- 主要终点
- area under the receiver operating characteristic curve of the deep learning system
研究概览
简要总结
Artificial Intelligence may provide insight into exploring the potential covert association behind and reveal some early ocular architecture changes in individuals with hepatobiliary disorders. We conducted a pioneer work to explore the association between the eye and liver via deep learning, to develop and evaluate different deep learning models to predict the hepatobiliary disease by using ocular images.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Prospective
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •The quality of fundus and slit-lamp images should clinical acceptable.
- •More than 90% of the fundus image area including four main regions (optic disk, macular, upper and lower retinal vessel archs) are easy to read and discriminate.
- •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
- •Images with light leakage (>10% of the area), spots from lens flares or stains, and overexposure were excluded from further analysis.
结局指标
主要结局
area under the receiver operating characteristic curve of the deep learning system
时间窗: baseline
The investigators will calculate the area under the receiver operating characteristic curve of deep learning system and compare this index between deep learning system and human doctors
次要结局
- sensitivity and specificity of the deep learning system(baseline)
研究者
Haotian Lin
Principal Investigator
Sun Yat-sen University
研究点 (1)
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