Detection of Jaundice From Ocular Images Via Deep Learning : a Prospective, Multicenter Cohort Study
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 1,633
- 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
Our study presents a detection model predicting a diagnosis of jaundice (clinical jaundice and occult jaundice) trained on prospective cohort data from slit-lamp photos and smartphone photos, demonstrating the model's validity and assisting clinical workers in identifying patient underlying hepatobiliary diseases.
详细描述
This study demonstrated that deep learning models could detect jaundice using ocular images in blood levels with reasonable accuracy, providing a non-invasive method for jaundice detection and recognition. This algorithm can assist clinical surgeons with daily follow-up visits and provide referral advice. It also highlights the algorithm's potential smartphone application in sizeable real-world population-based disease-detecting or telemedicine programs.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •The quality of slit-lamp images should be clinical acceptable. More than 90% of the slit-lamp image area, including three central 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
次要结局
- sensitivity and specificity of the deep learning system(baseline)
研究者
Haotian Lin
Principal Investigator
Sun Yat-sen University