Nephrology

Machine-learning-derived phenotypes of hypertensive patients using multidimensional clinical and echocardiographic data including strain imaging.

AIMS: We applied unsupervised machine learning clustering to a large cohort of hypertensive patients...

A machine learning model for accurate prediction of CMV end-organ disease.

BACKGROUND: Cytomegalovirus (CMV) End-Organ Disease (EOD) remains a significant complication in immu...

Renal graft imaging: An update and overview.

Imaging of renal graft function and pathology, including delayed graft function (DGF), often require...

Updates on Kidney Transplantation From the World Transplant Congress 2025.

The World Transplant Congress 2025 showcased pivotal advances shaping the future of transplantation....

Deep Learning Identification of Clear Cell Renal Cell Carcinoma on MR Imaging.

OBJECTIVE: To investigate the performance of various convolutional neural networks (CNNs) in identif...

Exfoliated kidney cells from urine for non-invasive kidney transplant monitoring: A potential opportunity?

Kidney transplantation is usually the optimal treatment option for patients living with kidney failu...

Cardiovascular Risk Assessment Tools in Chronic Kidney Disease.

Cardiovascular (CV) risk calculators estimate the likelihood of CV events by integrating factors suc...

Radiomics for the Prediction of Postoperative Chronic Kidney Disease in Renal Tumor Patients undergoing Surgical Resection.

OBJECTIVE: Chronic kidney disease (CKD) is a significant concern following renal tumor surgery, impa...

Derivation and validation of a machine learning-driven score to predict the diagnostic yield of endomyocardial biopsy.

Despite its low diagnostic yield, endomyocardial biopsy (EMB) remains the gold standard for establis...

Challenging a clinical dogma with multimodal machine learning: a retrospective analysis of transplant mismatched donor selection.

Advances in the postoperative care of the liver transplant recipient.

PURPOSE OF REVIEW: Survival rates following liver transplantation now exceed 90% at one year. Howeve...

Risk prediction in IgA nephropathy: from conventional models to machine learning, deep learning, and precision nephrology.

IgA nephropathy (IgAN) is the most prevalent primary glomerular disease worldwide and a leading caus...

Machine learning-driven computational drug repurposing to identify new tubulin inhibitors against cancer.

Tubulin is a validated anticancer target, yet the clinical translation of colchicine-binding site in...

Urinary metal exposure, systemic inflammation, and advanced cardiovascular-kidney-metabolic syndrome risk in US adults.

To examine inflammatory biomarkers as potential mediators in the association between urinary metal e...

Factors Associated with Machine Learning-Based Predictions of Retinal Aging Using Teleretinal Screening Images from Patients with Diabetes.

PURPOSE: To identify factors associated with accelerated retinal aging based on machine learning pre...

Development and internal validation of machine learning in predicting prognosis of acute kidney injury patients in resource-limited setting.

BACKGROUND: Machine learning models for predicting acute kidney injury (AKI) prognosis have primaril...

The Rise of Aqueous Selenium-Based Batteries: Challenges, Strategies, and the Path Forward.

Aqueous metal-selenium batteries (AMSeBs) have emerged as promising candidates for safe, cost-effect...

Predicting recurrent cardiac arrest within one year after surviving In-Hospital Cardiac Arrest using a machine learning model.

BACKGROUND: Patients discharged alive after in-hospital cardiac arrest (IHCA) have an increased mort...

Comment on "Machine learning survival analysis for predicting kidney disease progression in patients with acute kidney injury undergoing continuous kidney replacement therapy: An analysis of the LINKA database".

Machine Learning Models for Posttransplant Lymphoproliferative Disorder (PTLD) Risk Prediction in Thoracic Transplantation.

Posttransplant lymphoproliferative disorder (PTLD) is the second most common malignancy in thoracic ...

Interpretable deep learning model of circulating genomics for quantitative survival prediction in advanced non-small cell lung cancer.

PURPOSE: Accurate quantitative survival prediction in advanced non-small cell lung cancer (NSCLC) re...