Nephrology

Impact of tumor contact surface area on collecting system entry in robot-assisted partial nephrectomy: a retrospective analysis.

BACKGROUND: Collecting system entry in robot-assisted partial nephrectomy may occur even in cases showing a low N factor in the R.E.N.A.L nephrometry score. Therefore, in this study, we focused on the tumor contact surface area with the adjacent renal parenchyma and attempted to construct a novel predictive model for collecting system entry.

Muscle magnetic resonance characterization of STIM1 tubular aggregate myopathy using unsupervised learning.

PURPOSE: Congenital myopathies are a heterogeneous group of diseases affecting the skeletal muscles and characterized by high clinical, genetic, and histological variability. Magnetic Resonance (MR) is a valuable tool for the assessment of involved muscles (i.e., fatty replacement and oedema) and disease progression. Machine Learning is becoming increasingly applied for diagnostic purposes, but to...

Effective deep learning classification for kidney stone using axial computed tomography (CT) images.

INTRODUCTION: Stone formation in the kidneys is a common disease, and the high rate of recurrence and morbidity of the disease worries all patients wi...

Survey and Evaluation of Hypertension Machine Learning Research.

Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision-making. However, applications in di...

Long term experience of robotic retroperitoneal partial nephrectomy as the default approach in the management of renal masses: should the paradigm shift?

Although retroperitoneal surgery has demonstrated a better quality of recovery compared to transperitoneal routes, Retroperitoneal Robot Assisted Part...

Preliminary evaluation of deep learning for first-line diagnostic prediction of tumor mutational status.

The detection of tumour gene mutations by DNA or RNA sequencing is crucial for the prescription of effective targeted therapies. Recent developments s...

Body composition predicts hypertension using machine learning methods: a cohort study.

We used machine learning methods to investigate if body composition indices predict hypertension. Data from a cohort study was used, and 4663 records ...

First Comparison of Retroperitoneal Versus Transperitoneal Robot-Assisted Nephroureterectomy with Bladder Cuff: A Single Center Study.

INTRODUCTION: After recent presentation of the first complete robot-assisted retroperitoneal nephroureterectomy with bladder cuff (RRNU) for patients ...

Differential diagnosis of secondary hypertension based on deep learning.

Secondary hypertension is associated with higher risks of target organ damage and cardiovascular and cerebrovascular disease events. Early aetiology i...

The role of warm ischemia time on functional outcomes after robotic partial nephrectomy: a radionuclide renal scan study from the clock randomized trial.

PURPOSE: To evaluate the relationship between warm ischemia time (WIT) duration and renal function after robot-assisted partial nephrectomy (RAPN).

High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury.

Recovery from acute kidney injury can vary widely in patients and in animal models. Immunofluorescence staining can provide spatial information about ...

Robot-assisted radical nephrectomy for Wilms' tumor in children.

INTRODUCTION: Surgical removal of the tumor is a key step in the management of nephroblastoma. Less invasive surgical approaches such as robot-assiste...

Relating process and outcome metrics for meaningful and interpretable cannulation skill assessment: A machine learning paradigm.

BACKGROUND AND OBJECTIVES: The quality of healthcare delivery depends directly on the skills of clinicians. For patients on hemodialysis, medical erro...

Nonparametric failure time: Time-to-event machine learning with heteroskedastic Bayesian additive regression trees and low information omnibus Dirichlet process mixtures.

Many popular survival models rely on restrictive parametric, or semiparametric, assumptions that could provide erroneous predictions when the effects ...

Assessing the Effects of Deep Learning Reconstruction on Abdominal CT Without Arm Elevation.

To evaluate the effects of deep learning reconstruction (DLR) on image quality of abdominal computed tomography (CT) in patients without arm elevatio...

Comparison of Transperitoneal and Retroperitoneal Partial Nephrectomy with Single-Port Robot.

To investigate the efficacy and safety of single-port (SP) robotic transperitoneal (TP) and retroperitoneal (RP) partial nephrectomy. We sequentiall...

A novel multiplex score to predict outcomes of partial nephrectomy for multiple tumors.

BACKGROUND: The RENAL nephrometry score (RNS) is widely used to describe renal mass complexity and inform patient counseling for partial nephrectomy (...

A novel dataset and efficient deep learning framework for automated grading of renal cell carcinoma from kidney histopathology images.

Trends of kidney cancer cases worldwide are expected to increase persistently and this inspires the modification of the traditional diagnosis system t...

Glomerulus Detection Using Segmentation Neural Networks.

Digital pathology is vital for the correct diagnosis of kidney before transplantation or kidney disease identification. One of the key challenges in k...

Early Postoperative Outcomes of Retroperitoneal Partial Nephrectomy of Anterior and Posterior Renal Tumors: A 5-Year Experience in A Single Center.

Partial nephrectomy (PN) is one of the surgical treatment options for renal tumors. Therefore, the aim of this study was to compare the surgical outc...