ArticleNPJ digital medicine2023
Non-invasive chronic kidney disease risk stratification tool derived from retina-based deep learning and clinical factors.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Transformative applications of oculomics-based AI approaches in the management of systemic diseases: A systematic review.Journal of advanced research · 2025Pooled it
- Artificial intelligence in diabetic retinopathy: from automated screening to risk-stratified care.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Review
- The retina-body axis: proteomic mechanisms linking oculomics and clinical traits in a female aging cohort.npj aging · 2026Article
- Routine blood tests and machine learning identify complications in high myopia.Nature communications · 2026Article
- Retinal BioAge is associated with indicators of cardiovascular-kidney-metabolic syndrome in UK and US populations.Scientific reports · 2026Article
- Deep learning-derived retinal biomarker associated with diabetes-related amputation in type 2 diabetes.Frontiers in endocrinology · 2026Observational
- Eye and Systemic Disease Management Changes After Teleophthalmology Screening in Primary Care: Retrospective Cross-Sectional Pilot Study of 200 Consecutive Patients.JMIR formative research · 2025Article
- Phenotypic screening and genetic insights for predicting major vascular-related diseases using retinal imaging.NPJ digital medicine · 2025Article
- Identification and validation of an explainable early-stage chronic kidney disease prediction model: a multicenter retrospective study.EClinicalMedicine · 2025Article
- High-resolution fundus images for ophthalmomics and early cardiovascular disease prediction.Scientific data · 2025Article
- Use of artificial intelligence with retinal imaging in screening for diabetes-associated complications: systematic review.EClinicalMedicine · 2025Review
- Diagnosis of Chronic Kidney Disease Using Retinal Imaging and Urine Dipstick Data: Multimodal Deep Learning Approach.JMIR medical informatics · 2025Article
- Artificial intelligence-enhanced retinal imaging as a biomarker for systemic diseases.Theranostics · 2025Review
- Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging.Eye and vision (London, England) · 2024Review
- Simultaneous detection of multiple urinary biomarkers in patients with early-stage diabetic kidney disease using Luminex liquid suspension chip technology.Frontiers in endocrinology · 2024Article
- Article
- Applications of artificial intelligence-assisted retinal imaging in systemic diseases: A literature review.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 <90 mL/min/1.73 m
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.