The automatic detection of diabetic kidney disease from retinal vascular parameters combined with clinical variables using artificial intelligence in type-2 diabetes patients

Prediction of diabetic kidney disease with machine learning algorithms, upon the initial diagnosis of type 2 diabetes mellitus

(2022) Angier Allen et al.   BMJ Open Diabetes Research & Care

Association of visceral adiposity index with incident nephropathy and retinopathy: a cohort study in the diabetic population

(2022) Zhiyuan Wu et al.   Cardiovascular Diabetology

Prediction of 3-year risk of diabetic kidney disease using machine learning based on electronic medical records

(2022) Zheyi Dong et al.   Journal of Translational Medicine

Comparison of Nonalbuminuric and Albuminuric Diabetic Kidney Disease Among Patients With Type 2 Diabetes: A Systematic Review and Meta-Analysis

(2022) Shaomin Shi et al.   Frontiers in Endocrinology

Novel biomarkers and therapeutic approaches for diabetic retinopathy and nephropathy: Recent progress and future perspectives

(2022) Ziyan Xie et al.   Frontiers in Endocrinology

Deep learning for the classification of medical kidney disease: a pilot study for electron microscopy

(2021) Sean Hacking et al.   ULTRASTRUCTURAL PATHOLOGY

Investigation of associations between retinal microvascular parameters and albuminuria in UK Biobank: a cross-sectional case-control study

(2021) Euan N. Paterson et al.   BMC Nephrology

Retinal Microcirculation Alterations in Microalbuminuric Diabetic Patients With and Without Retinopathy

(2021) Cemile Ucgul Atilgan et al.   Seminars in Ophthalmology

Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease

(2021) Lili Chan et al.   DIABETOLOGIA

Correlation between retinal vascular parameters and cystatin C in patients with type 2 diabetes

(2021) Qiong Wang et al.   ACTA DIABETOLOGICA

Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images

(2021) Kang Zhang et al.   Nature Biomedical Engineering

Retinal Vascular Signs as Screening and Prognostic Factors for Chronic Kidney Disease: A Systematic Review and Meta-Analysis of Current Evidence

(2021) Michael Aronov et al.   Journal of Personalized Medicine

Risk prediction of diabetic nephropathy using machine learning techniques: A pilot study with secondary data

(2021) Md. Maniruzzaman et al.   Diabetes & Metabolic Syndrome-Clinical Research & Reviews

Machine learning approach to predicting albuminuria in persons with type 2 diabetes: An analysis of the LOOK AHEAD Cohort

(2021) Zeid Khitan et al.   Journal of Clinical Hypertension

A stratified analysis of a deep learning algorithm in the diagnosis of diabetic retinopathy in a real‐world study

(2021) Na Li et al.   Journal of Diabetes

DIABETIC RETINOPATHY, CLASSIFIED USING THE LESION-AWARE DEEP LEARNING SYSTEM, PREDICTS DIABETIC END-STAGE RENAL DISEASE IN CHINESE PATIENTS

(2020) Lijun Zhao et al.   Endocrine Practice

Comprehensive retinal vascular measurements: a novel association with renal function in type 2 diabetic patients in China

(2020) Xiayu Xu et al.   Scientific Reports

Deep Learning Could Diagnose Diabetic Nephropathy with Renal Pathological Immunofluorescent Images

(2020) Shinji Kitamura et al.   Diagnostics

Retinal image measurements and their association with chronic kidney disease in Chinese patients with type 2 diabetes: the NCD study

(2020) Xiayu Xu et al.   ACTA DIABETOLOGICA

Machine‐learning–based early prediction of end‐stage renal disease in patients with diabetic kidney disease using clinical trials data

(2020) Sunil Belur Nagaraj et al.   DIABETES OBESITY & METABOLISM

Prediction of Nephropathy in Type 2 Diabetes: An Analysis of the ACCORD Trial applying Machine Learning Techniques

(2019) Violeta Rodriguez‐Romero et al.   CTS-Clinical and Translational Science

2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD

(2019) Francesco Cosentino et al.   EUROPEAN HEART JOURNAL

Detection of Lower Albuminuria Levels and Early Development of Diabetic Kidney Disease Using an Artificial Intelligence-Based Rule Extraction Approach

(2019) Yoichi Hayashi   Diagnostics

Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning

(2019) Masaki Makino et al.   Scientific Reports

Evaluation of glomerular filtration rate by different equations in Chinese elderly with chronic kidney disease

(2016) Guan Changjie et al.   INTERNATIONAL UROLOGY AND NEPHROLOGY

An interpretable rule-based diagnostic classification of diabetic nephropathy among type 2 diabetes patients

(2015) Guan-Mau Huang et al.   BMC BIOINFORMATICS

Using a multi-staged strategy based on machine learning and mathematical modeling to predict genotype-phenotype risk patterns in diabetic kidney disease: a prospective case–control cohort analysis

(2013) Ross KK Leung et al.   BMC Nephrology

Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods

(2007) Baek Hwan Cho et al.   ARTIFICIAL INTELLIGENCE IN MEDICINE