Nephrology

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

OBJECTIVE: To investigate the performance of various convolutional neural networks (CNNs) in identifying clear renal cell carcinoma (ccRCC) on MRI and to compare them with radiologists using the clear cell likelihood score (ccLS) algorithm. METHODS: A total of 480 CNN models were retrospectively trained using 1 or 3 (fusion) different types of MR images obtained from 310 patients with pathological...

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 failure given its associations with improved survival, quality of life outcomes and a reduction in the personal, economic, and societal burden of long-term dialysis. While advantages of kidney transplantation are recognized, post-transplant complications, such as graft rejection, ischemia-reperfusion inj...

Cardiovascular Risk Assessment Tools in Chronic Kidney Disease.

Cardiovascular (CV) risk calculators estimate the likelihood of CV events by integrating factors such as age, sex, blood pressure, lipids, smoking, an...

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, impacting long-term renal function and patient outcome...

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 establishing a definitive diagnosis in many cardiomyopathi...

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. However, the patient group undergoing liver transplantat...

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 cause of end-stage kidney disease (ESKD). Its clinical...

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 inhibitors remains limited by toxicity and resistanc...

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 exposure and advanced Cardiovascular-Kidney-Metabol...

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 predictions of age using fundus images from teleretin...

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 primarily been developed in resource-rich settings, with l...

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-effective, and high-energy-density energy storage, yet t...

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 mortality up to a year after hospital discharge. Impro...

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 transplant recipients and is associated with poor ...

Letter: Deep Learning Model Significantly Improves MAGGIC Score for Predicting Contrast-Induced Nephropathy in STEMI Patients.

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) remains an unmet clinical need. While liquid biopsy ...

Developing and validating a machine learning model for predicting post-thrombolysis seizures in acute ischemic stroke.

Post-stroke seizures (PSS) manifests variably due to ischemic brain injury, yet its risk factors remain unclear. This study developed a machine learni...

Few-Shot Pulmonary Vessel Segmentation based on Tubular-Aware Prompt-Tuning.

Segmentation of the pulmonary vessel from computed tomography (CT) images plays a crucial role in the diagnosis and treatment of various lung diseases...