Nephrology

Predicting chronic kidney disease progression with artificial intelligence.

BACKGROUND: The use of tools that allow estimation of the probability of progression of chronic kidney disease (CKD) to advanced stages has not yet achieved significant practical importance in clinical setting. This study aimed to develop and validate a machine learning-based model for predicting the need for renal replacement therapy (RRT) and disease progression for patients with stage 3-5 CKD.

Study on design optimization of GFRP tubular column composite structure based on machine learning method.

Circular reinforced concrete wound glass fiber reinforced polymer (GFRP) columns and reinforced concrete filled GFRP columns are extensively utilized in civil engineering practice. Various factors influence the performance of these two types of GFRP columns, thereby impacting the whole project. Therefore, it is highly significant to establish the prediction models for ultimate displacement and ult...

Predicting osteoporosis from kidney-ureter-bladder radiographs utilizing deep convolutional neural networks.

Osteoporosis is a common condition that can lead to fractures, mobility issues, and death. Although dual-energy X-ray absorptiometry (DXA) is the gold...

Predicting clinical outcomes of SARS-CoV-2 infection during the Omicron wave using machine learning.

The Omicron SARS-CoV-2 variant continues to strain healthcare systems. Developing tools that facilitate the identification of patients at highest risk...

A systematic review of prediction models on arteriovenous fistula: Risk scores and machine learning approaches.

OBJECTIVE: Failure-to-mature and early stenosis remains the Achille's heel of hemodialysis arteriovenous fistula (AVF) creation. The maturation and pa...

Application of interpretable machine learning algorithms to predict acute kidney injury in patients with cerebral infarction in ICU.

BACKGROUND: Acute kidney injury (AKI) is not only a complication but also a serious threat to patients with cerebral infarction (CI). This study aimed...

An explainable machine learning model to predict early and late acute kidney injury after major hepatectomy.

BACKGROUND: Risk assessment models for acute kidney injury (AKI) after major hepatectomy that differentiate between early and late AKI are lacking. Th...

Predictive modeling of co-infection in lupus nephritis using multiple machine learning algorithms.

This study aimed to analyze peripheral blood lymphocyte subsets in lupus nephritis (LN) patients and use machine learning (ML) methods to establish an...

Effects of tacrolimus on proteinuria in Chinese and Indian patients with idiopathic membranous nephropathy: the results of machine learning study.

PURPOSE: The present study aims to explore the effects of tacrolimus on proteinuria in patients with idiopathic membranous nephropathy (IMN) and recom...

Machine learning algorithms to predict colistin-induced nephrotoxicity from electronic health records in patients with multidrug-resistant Gram-negative infection.

OBJECTIVES: Colistin-induced nephrotoxicity prolongs hospitalisation and increases mortality. The study aimed to construct machine learning models to ...

Development of Novel Methods for QSAR Modeling by Machine Learning Repeatedly: A Case Study on Drug Distribution to Each Tissue.

Artificial intelligence is expected to help identify excellent candidates in drug discovery. However, we face a lack of data, as it is time-consuming ...

Deep-learning model for evaluating histopathology of acute renal tubular injury.

Tubular injury is the most common cause of acute kidney injury. Histopathological diagnosis may help distinguish between the different types of acute ...

Renal artery pseudoaneurysm following robot assisted nephron sparing surgery: two case reports.

BACKGROUND: Renal artery pseudoaneurysm following partial nephrectomy is a rare entity, the incidence of this entity is more common following penetrat...

Deep learning prediction of renal anomalies for prenatal ultrasound diagnosis.

Deep learning algorithms have demonstrated remarkable potential in clinical diagnostics, particularly in the field of medical imaging. In this study, ...

Radiomics and machine learning for renal tumor subtype assessment using multiphase computed tomography in a multicenter setting.

OBJECTIVES: To distinguish histological subtypes of renal tumors using radiomic features and machine learning (ML) based on multiphase computed tomogr...

Reproducibility and prognostic ability of chronicity parameters in kidney biopsy - Comprehensive evaluation comparing microscopy and artificial intelligence in digital pathology.

INTRODUCTION: Semi-quantitative scoring of various parameters in renal biopsy is accepted as an important tool to assess disease activity and prognost...

UroAngel: a single-kidney function prediction system based on computed tomography urography using deep learning.

BACKGROUND: Accurate estimation of the glomerular filtration rate (GFR) is clinically crucial for determining the status of obstruction, developing tr...

Assessing the perioperative outcomes of abdominal drain omission after robot-assisted partial nephrectomy.

The study aimed to evaluate the impact of abdominal drain placement (vs. omission) on perioperative outcomes of robot-assisted partial nephrectomy (RA...

Text mining of hypertension researches in the west Asia region: a 12-year trend analysis.

More than half of the world population lives in Asia and hypertension (HTN) is the most prevalent risk factor found in Asia. There are numerous articl...

The application of different machine learning models based on PET/CT images and EGFR in predicting brain metastasis of adenocarcinoma of the lung.

OBJECTIVE: To explore the value of six machine learning models based on PET/CT radiomics combined with EGFR in predicting brain metastases of lung ade...