Nephrology

Evolution of renal function in patients with severe intestinal failure on home parenteral nutrition.

BACKGROUND: Kidney disease is a frequent but underestimated complication in patients suffering from intestinal failure (IF) treated by long-term home parenteral nutrition (HPN). The evolution in glomerular filtration rate (GFR) over time is poorly characterized. The current equations for estimating GFR have limited precision. No study has specifically investigated the reliability of recent creatin...

Inspection of visible components in urine based on deep learning.

PURPOSE: Urinary particles are particularly important parameters in clinical urinalysis, especially for the diagnosis of nephropathy. Therefore, it is highly important to precisely detect urinary particles in the clinical setting. However, artificial microscopy is subjective and time consuming, and various previous detection algorithms lack the adequate accuracy. In this study, a method is propose...

Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China.

Retinal fundus photography provides a non-invasive approach for identifying early microcirculatory alterations of chronic diseases prior to the onset ...

Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study.

BACKGROUND: Sickle cell disease (SCD) is a genetic disorder of the red blood cells, resulting in multiple acute and chronic complications, including p...

A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations.

BACKGROUND: Screening for chronic kidney disease is a challenge in community and primary care settings, even in high-income countries. We developed an...

Deep learning based classification of solid lipid-poor contrast enhancing renal masses using contrast enhanced CT.

OBJECTIVE: Establish a workflow that utilizes convolutional neural nets (CNN) to classify solid, lipid-poor, contrast enhancing renal masses using mul...

Prediction of the Mortality Risk in Peritoneal Dialysis Patients using Machine Learning Models: A Nation-wide Prospective Cohort in Korea.

Herein, we aim to assess mortality risk prediction in peritoneal dialysis patients using machine-learning algorithms for proper prognosis prediction. ...

How to do it: a robotic kidney autotransplant.

We describe Australia's first reported case of robotic kidney autotransplantation for a complex renal artery aneurysm. It is potentially a safe, minim...

Re: Geraldine Pignot, Antoine Thiery-Vuillemin, Jochen Walz, et al. Nephrectomy After Complete Response to Immune Checkpoint Inhibitors for Metastatic Renal Cell Carcinoma: A New Surgical Challenge? Eur Urol. In press. https://doi.org/10.1016/j.eururo.2019.12.018: The Next Surgical Frontier in Kidney Cancer: Nephrectomy After Immune Checkpoint Inhibition.

Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion.

OBJECTIVES: To develop a deep learning-based method for automated classification of renal cell carcinoma (RCC) from benign solid renal masses using co...

Neurotransmitter networks in mouse prefrontal cortex are reconfigured by isoflurane anesthesia.

This study quantified eight small-molecule neurotransmitters collected simultaneously from prefrontal cortex of C57BL/6J mice ( = 23) during wakefulne...

Predicting severe clinical events by learning about life-saving actions and outcomes using distant supervision.

Medical error is a leading cause of patient death in the United States. Among the different types of medical errors, harm to patients caused by doctor...

Artificial intelligence for early prediction of pulmonary hypertension using electrocardiography.

BACKGROUND: Screening and early diagnosis of pulmonary hypertension (PH) are critical for managing progression and preventing associated mortality; ho...

Surgical details and renal function change after robot-assisted partial nephrectomy.

OBJECTIVES: To investigate whether differences in surgical details during robot-assisted partial nephrectomy impact postoperative renal function.

Cov_FB3D: A De Novo Covalent Drug Design Protocol Integrating the BA-SAMP Strategy and Machine-Learning-Based Synthetic Tractability Evaluation.

drug design actively seeks to use sets of chemical rules for the fast and efficient identification of structurally new chemotypes with the desired se...

Characterization of sodium removal to ultrafiltration volume in a peritoneal dialysis outpatient cohort.

BACKGROUND: Failure to control volume is the second most common cause of peritoneal dialysis (PD) technique failure. Sodium is primarily removed by co...

Role of Artificial Intelligence in Kidney Disease.

Artificial intelligence (AI), as an advanced science technology, has been widely used in medical fields to promote medical development, mainly applied...

Artificial intelligence and machine learning in nephropathology.

Artificial intelligence (AI) for the purpose of this review is an umbrella term for technologies emulating a nephropathologist's ability to extract in...

Machine learning, the kidney, and genotype-phenotype analysis.

With biomedical research transitioning into data-rich science, machine learning provides a powerful toolkit for extracting knowledge from large-scale ...

Clinical decision support system to predict chronic kidney disease: A fuzzy expert system approach.

BACKGROUND AND OBJECTIVES: Diagnosis and early intervention of chronic kidney disease are essential to prevent loss of kidney function and a large amo...