AIMC Topic: Diabetic Nephropathies

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Exploring the subtle and novel renal pathological changes in diabetic nephropathy using clustering analysis with deep learning.

Scientific reports
To decrease the number of chronic kidney disease (CKD), early diagnosis of diabetic kidney disease is required. We performed invariant information clustering (IIC)-based clustering on glomerular images obtained from nephrectomized kidneys of patients...

Prognostic Features for Overall Survival in Male Diabetic Patients Undergoing Hemodialysis Using Elastic Net Penalized Cox Regression; A Machine Learning Approach.

Archives of Iranian medicine
BACKGROUND: Diabetics constitute a significant percentage of hemodialysis (HD) patients with higher mortality, especially among male patients. A machine learning algorithm was used to optimize the prediction of time to death in male diabetic hemodial...

An arterial spin labeling-based radiomics signature and machine learning for the prediction and detection of various stages of kidney damage due to diabetes.

Frontiers in endocrinology
OBJECTIVE: The aim of this study was to assess the predictive capabilities of a radiomics signature obtained from arterial spin labeling (ASL) imaging in forecasting and detecting stages of kidney damage in patients with diabetes mellitus (DM), as we...

A machine learning model for predicting worsening renal function using one-year time series data in patients with type 2 diabetes.

Journal of diabetes investigation
BACKGROUND AND AIMS: To prevent end-stage renal disease caused by diabetic kidney disease, we created a predictive model for high-risk patients using machine learning.

Unveiling the utility of artificial intelligence for prediction, diagnosis, and progression of diabetic kidney disease: an evidence-based systematic review and meta-analysis.

Current medical research and opinion
OBJECTIVE: The purpose of this study was to conduct a systematic investigation of the potential of artificial intelligence (AI) models in the prediction, detection of diagnostic biomarkers, and progression of diabetic kidney disease (DKD). In additio...

Development and external validation of a machine learning model to predict diabetic nephropathy in T1DM patients in the real-world.

Acta diabetologica
AIMS: Studies on machine learning (ML) for the prediction of diabetic nephropathy (DN) in type 1 diabetes mellitus (T1DM) patients are rare. This study focused on the development and external validation of an explainable ML model to predict the risk ...

Development of a machine learning model for precision prognosis of rapid kidney function decline in people with diabetes and chronic kidney disease.

Diabetes research and clinical practice
AIMS: To develop a machine learning model for predicting rapid kidney function decline in people with type 2 diabetes (T2D) and chronic kidney disease (CKD) and to pinpoint key modifiable risk factors for targeted interventions.

Self-Supervised Learning for Feature Extraction from Glomerular Images and Disease Classification with Minimal Annotations.

Journal of the American Society of Nephrology : JASN
BACKGROUND: Deep learning has great potential in digital kidney pathology. However, its effectiveness depends heavily on the availability of extensively labeled datasets, which are often limited because of the specialized knowledge and time required ...

Integrated approach of machine learning, Mendelian randomization and experimental validation for biomarker discovery in diabetic nephropathy.

Diabetes, obesity & metabolism
AIM: To identify potential biomarkers and explore the mechanisms underlying diabetic nephropathy (DN) by integrating machine learning, Mendelian randomization (MR) and experimental validation.

Identification of common biomarkers in diabetic kidney disease and cognitive dysfunction using machine learning algorithms.

Scientific reports
Cognitive dysfunction caused by diabetes has become a serious global medical issue. Diabetic kidney disease (DKD) exacerbates cognitive dysfunction in patients, although the precise mechanism behind this remains unclear. Here, we conducted an investi...