AIMC Topic: Glomerular Filtration Rate
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Personalized prediction model generated with machine learning for kidney function one year after living kidney donation.
Scientific reports
Jul 1, 2025
Living kidney donors typically experience approximately a 30% reduction in kidney function after donation, although the degree of reduction varies among individuals. This study aimed to develop a machine learning (ML) model to predict serum creatinin...
Predicting rapid kidney function decline in middle-aged and elderly Chinese adults using machine learning techniques.
BMC medical informatics and decision making
Jun 6, 2025
The rapid decline of kidney function in middle-aged and elderly people has become an increasingly serious public health problem. Machine learning (ML) technology has substantial potential to disease prediction. The present study use dataset from the ...
Development and validation of multi-center serum creatinine-based models for noninvasive prediction of kidney fibrosis in chronic kidney disease.
Renal failure
Apr 15, 2025
OBJECTIVE: Kidney fibrosis is a key pathological feature in the progression of chronic kidney disease (CKD), traditionally diagnosed through invasive kidney biopsy. This study aimed to develop and validate a noninvasive, multi-center predictive model...
A comparison of neural networks and regression-based approaches for estimating kidney function in pediatric chronic kidney disease: Practical predictive epidemiology for clinical management of a progressive disease.
Annals of epidemiology
Apr 8, 2025
PURPOSE: Clinical management of pediatric chronic kidney disease requires estimation of glomerular filtration rate (eGFR). Currently, eGFR is determined by two endogenous markers measured in blood: serum creatine (SCr) and cystatin C (CysC). Machine ...
Machine learning algorithms for diabetic kidney disease risk predictive model of Chinese patients with type 2 diabetes mellitus.
Renal failure
Apr 7, 2025
BACKGROUND: Diabetic kidney disease (DKD) is a common and serious complication of diabetic mellitus (DM). More sensitive methods for early DKD prediction are urgently needed. This study aimed to set up DKD risk prediction models based on machine lear...
A neural network approach to glomerular filtration rate estimation: a single-centre retrospective audit.
Nuclear medicine communications
Apr 7, 2025
OBJECTIVES: The 2009 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation without race correction factor is frequently used for an estimate of glomerular filtration rate (eGFR) and to support a single-sample GFR regime. This study exa...
Contrast-Induced Acute Kidney Injury in Lower Limb Percutaneous Transluminal Angioplasty: A Machine Learning Approach for Preoperative Risk Prediction.
Annals of vascular surgery
Mar 11, 2025
BACKGROUND: Contrast-induced acute kidney injury (CI-AKI) is a common complication of lower limb percutaneous transluminal angioplasty (PTA). Common risk models are based on cardiology cohorts for percutaneous coronary intervention. They include a mi...
Lower Extremity
Humans
Machine Learning
Peripheral Arterial Disease
Predictive Value of Tests
Reproducibility of Results
Angioplasty
Risk Factors
Decision Support Techniques
Aged
Risk Assessment
Retrospective Studies
Male
Treatment Outcome
Acute Kidney Injury
Middle Aged
Glomerular Filtration Rate
Female
Contrast Media
A recursive embedding and clustering technique for unraveling asymptomatic kidney disease using laboratory data and machine learning.
Scientific reports
Feb 17, 2025
Traditional methods for diagnosing chronic kidney disease (CKD) via laboratory data may not be capable of identifying early kidney disease. Kidney biopsy is unsuitable for regular screening, and imaging tests are costly and time-consuming. Several st...
Diagnosis of Chronic Kidney Disease Using Retinal Imaging and Urine Dipstick Data: Multimodal Deep Learning Approach.
JMIR medical informatics
Feb 7, 2025
BACKGROUND: Chronic kidney disease (CKD) is a prevalent condition with significant global health implications. Early detection and management are critical to prevent disease progression and complications. Deep learning (DL) models using retinal image...
A Fully Automated Artificial Intelligence-Based Approach to Predict Renal Function After Radical or Partial Nephrectomy.
Urology
Feb 4, 2025
OBJECTIVE: To test if our artificial intelligence (AI)-postoperative glomerular filtration rate (GFR) prediction is as accurate as a validated clinical model. The American Urologic Association recommends estimating postoperative GFR in patients with ...
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