Building a healthier future: Artificial Intelligence-based early diagnosis of chronic kidney disease through retinal images
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 3,384
- 试验地点
- 1
- 主要终点
- serum creatinine and proteinuria
研究概览
简要总结
This study aims to optimize and validate an artificial intelligence (AI)-based model for the early diagnosis of chronic kidney disease (CKD) using retinal fundus images. A total of 3384 patients with CKD or diabetes or hypertension and control group will be enrolled at PGIMER, Chandigarh. Retinal images, clinical data, and blood/urine biomarkers will be collected, with blood samples also biobanked for future research. Data will be anonymized and analyzed by LifeBytes, Bengaluru, to develop and test the AI algorithm. The goal is to create a non-invasive, cost-effective, and scalable screening tool for early CKD detection, particularly suited for low-resource healthcare settings.
研究设计
- 研究类型
- Observational
入排标准
- 年龄范围
- 18.00 Year(s) 至 99.00 Year(s)（—）
- 性别
- All
入选标准
- •Patients with CKD (all stages), diabetes, or hypertension presenting at PGIMER, Chandigarh, will be recruited to identify retinal markers for early CKD diagnosis.
排除标准
- •Patients with a history of eye injury or prior eye surgery will be excluded.
结局指标
主要结局
serum creatinine and proteinuria
时间窗: baseline and three monthly till 2 years
次要结局
未报告次要终点
研究者
Smita Divyaveer
PGIMER Chandigarh