Non-invasive chronic kidney disease risk stratification tool derived from retina-based deep learning and clinical factors

Despite the importance of preventing chronic kidney disease (CKD), predicting high-risk patients who require active intervention is challenging, especially in people with preserved kidney function. In this study, a predictive risk score for CKD (Reti-CKD score) was derived from a deep learning algor...

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Bibliographic Details
Published in:NPJ digital medicine 2023-06, Vol.6 (1), p.114-114, Article 114
Main Authors: Joo, Young Su, Rim, Tyler Hyungtaek, Koh, Hee Byung, Yi, Joseph, Kim, Hyeonmin, Lee, Geunyoung, Kim, Young Ah, Kang, Shin-Wook, Kim, Sung Soo, Park, Jung Tak
Format: Article
Language:English
Subjects:
692/308/53/2422
692/499
Biobanks
Biomedicine
Biotechnology
Deep learning
Diabetes
Kidney diseases
Medicine
Medicine & Public Health
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Summary:Despite the importance of preventing chronic kidney disease (CKD), predicting high-risk patients who require active intervention is challenging, especially in people with preserved kidney function. In this study, a predictive risk score for CKD (Reti-CKD score) was derived from a deep learning algorithm using retinal photographs. The performance of the Reti-CKD score was verified using two longitudinal cohorts of the UK Biobank and Korean Diabetic Cohort. Validation was done in people with preserved kidney function, excluding individuals with eGFR
ISSN:2398-6352
2398-6352
DOI:10.1038/s41746-023-00860-5