Nephrology

Predicting graft and patient outcomes following kidney transplantation using interpretable machine learning models.

The decision to accept a deceased donor organ offer for transplant, or wait for something potentially better in the future, can be challenging. Clinical decision support tools predicting transplant outcomes are lacking. This project uses interpretable methods to predict both graft failure and patient death using data from previously accepted kidney transplant offers. Using more than 25 years of tr...

Biomimetic Neuromorphic Sensory System via Electrolyte Gated Transistors.

Biomimetic neuromorphic sensing systems, inspired by the structure and function of biological neural networks, represent a major advancement in the field of sensing technology and artificial intelligence. This review paper focuses on the development and application of electrolyte gated transistors (EGTs) as the core components (synapses and neuros) of these neuromorphic systems. EGTs offer unique ...

Prediction of post-donation renal function using machine learning techniques and conventional regression models in living kidney donors.

BACKGROUND: Accurate prediction of renal function following kidney donation and careful selection of living donors are essential for living-kidney don...

RACGAP1 is a pivotal gene in lung adenocarcinoma-associated membranous nephropathy: Based on comprehensive bioinformatics analysis and machine learning.

BACKGROUND: This study performs a detailed bioinformatics and machine learning analysis to investigate the genetic foundations of membranous nephropat...

Multi-parametric MRI-based machine learning model for prediction of pathological grade of renal injury in a rat kidney cold ischemia-reperfusion injury model.

BACKGROUND: Renal cold ischemia-reperfusion injury (CIRI), a pathological process during kidney transplantation, may result in delayed graft function ...

Artificial intelligence in chronic kidney diseases: methodology and potential applications.

Chronic kidney disease (CKD) represents a significant global health challenge, characterized by kidney damage and decreased function. Its prevalence h...

Unlocking Tomorrow's Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence.

As an integral aspect of health care, digital technology has enabled modelling of complex relationships to detect, screen, diagnose, and predict patie...

Machine learning for the prediction of 1-year mortality in patients with sepsis-associated acute kidney injury.

INTRODUCTION: Sepsis-associated acute kidney injury (SA-AKI) is strongly associated with poor prognosis. We aimed to build a machine learning (ML)-bas...

Creatinine clearance in critically ill adults: prospective comparison of prediction by intensive care unit physicians and machine learning models.

A Boundary-Enhanced Decouple Fusion Segmentation Network for Diagnosis of Adenomatous Polyps.

Adenomatous polyps, a common premalignant lesion, are often classified into villous adenoma (VA) and tubular adenoma (TA). VA has a higher risk of mal...

Risk prediction of kalaemia disturbance and acute kidney injury after total knee arthroplasty: use of a machine learning algorithm.

INTRODUCTION: Total knee arthroplasty (TKA) is a procedure associated with risks of electrolyte and kidney function disorders, which are rare but can ...

Machine learning-based prediction of vancomycin concentration after abdominal administration in patients with peritoneal dialysis-related peritonitis.

INTRODUCTION: Peritonitis is a serious complication of peritoneal dialysis (PD), in which insufficient control of antibacterial drug concentrations po...

STC-UNet: renal tumor segmentation based on enhanced feature extraction at different network levels.

Renal tumors are one of the common diseases of urology, and precise segmentation of these tumors plays a crucial role in aiding physicians to improve ...

The Current Application and Future Potential of Artificial Intelligence in Renal Cancer.

Artificial intelligence (AI) is the integration of human tasks into machine processes. The role of AI in kidney cancer evaluation, management, and out...

Automated identification and segmentation of urine spots based on deep-learning.

Micturition serves an essential physiological function that allows the body to eliminate metabolic wastes and maintain water-electrolyte balance. The ...

Brief Review and Primer of Key Terminology for Artificial Intelligence and Machine Learning in Hypertension.

Recent breakthroughs in artificial intelligence (AI) have caught the attention of many fields, including health care. The vision for AI is that a comp...

Transforming Hypertension Diagnosis and Management in The Era of Artificial Intelligence: A 2023 National Heart, Lung, and Blood Institute (NHLBI) Workshop Report.

Hypertension is among the most important risk factors for cardiovascular disease, chronic kidney disease, and dementia. The artificial intelligence (A...

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods.

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mor...

Automatically Detecting Pancreatic Cysts in Autosomal Dominant Polycystic Kidney Disease on MRI Using Deep Learning.

BACKGROUND: Pancreatic cysts in autosomal dominant polycystic kidney disease (ADPKD) correlate with PKD2 mutations, which have a different phenotype t...

Smell cancer by machine learning-assisted peptide/MXene bioelectronic array.

Non-invasive detection of tumors is of utmost importance to save lives. Nonetheless, identifying tumors through gas analysis is a challenging task. In...