| Title: | An artificial intelligence-based approach for the early detection of chronic kidney diseases from digital retinal photographs |
| Lead Institution: | Khalifa University of Science and Technology |
| Principal investigator: |
Application 106155
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About
Chronic Kidney Disease (CKD) is a serious condition that can lead to kidney failure. Diabetes is the leading cause of CKD, and people with diabetes are advised to get regular eye exams to monitor for signs of retinopathy, which is linked to CKD. To make an early diagnosis of CKD easier, we can use artificial intelligence to analyze retinal images and predict the onset and stage of CKD. We will use deep-learning models to analyze the retinal images and collect clinical data from patients, such as age, blood pressure, and diabetes status, to predict their kidney function. We will use this information to train separate models to predict kidney function and blood glucose levels. Then, we will use these predicted values to detect CKD and its stages. Finally, we will combine the retinal image data and clinical information to create a more accurate model. Early detection is appealing because inexpensive intervention can ameliorate renal function early and prevent continuous deterioration. The shortcomings of traditional analysis methods make a new comprehensive assessment approach urgently needed in kidney disease patients and a large population scale. ML and DL combined with retinal imaging is a relatively prominent frontier in this research area.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 21087 | Optical coherence tomographic retinal differences across stages of chronic kidney disease | Samit Kumar Ghosh (+2) | 2026 | BioData Mining |
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