Nephrology

Machine Learning-Based Prediction of Postoperative Vasoplegia After Cardiac Surgery.

OBJECTIVE: To develop and internally validate a machine-learning model for the early prediction of postoperative vasoplegia after cardiac surgery. DESIGN: A retrospective cohort study with model development and internal validation. SETTING: Tertiary academic intensive care units (ICUs) using routinely collected perioperative and postoperative clinical data. PARTICIPANTS: Adults admitted to intensi...

Digital health integration in chronic kidney disease.

Chronic kidney disease (CKD) is a growing global health burden characterized by progressive renal dysfunction, high comorbidity rates, and substantial healthcare costs. Digital health technologies, including telemedicine, wearable biosensors, mobile health applications, and AI-driven decision support systems, offer transformative potential for improving CKD management across the care continuum. Th...

Ethical challenges in kidney transplantation.

PURPOSE OF REVIEW: This review provides a comprehensive examination of the current inequities and ethical challenges in kidney transplantation and hig...

Emerging risk factors for stroke and bleeding in patients with atrial fibrillation and heart failure-a narrative review.

Atrial fibrillation (AF) and heart failure (HF) frequently coexist, which leads to adverse clinical outcomes and a significant increase in the risk of...

Smart transplant+: A HyCARE hybrid AI-cloud framework for intelligent donor-recipient matching, workflow automation, and post-transplant optimization.

Organ transplantation is a life-saving medical intervention to reverse end-stage organ failure. Despite its life-saving potential, organ transplantati...

Artificial intelligence and transplanting the elderly.

PURPOSE OF REVIEW: The integration of artificial intelligence into allocation, organ retrieval and transplantation processes represents an innovative ...

Current status and future directions for the use of artificial intelligence in pediatric critical care nephrology.

Artificial intelligence (AI) has the potential to significantly improve the practice of medicine. However, its application in pediatric critical care ...

Interpretable machine learning model based on blood parameters for screening high myopia.

OBJECTIVE: To develop an interpretable machine learning (ML) model using routine blood parameters for high myopia (HM) screening as a convenient and c...

Context-aware heterogeneous graph neural network for multi-level description and invasiveness prediction in renal cell carcinoma.

The invasiveness prediction in renal cell carcinoma (RCC) is of significant importance for the decision of clinical surgical plans and the patients' p...

Hereditary syndromes and RCC: what radiologists need to know.

Hereditary renal cell carcinoma (RCC) accounts for approximately 5-8% of all renal cancers. This review provides a comprehensive overview of the seven...

Functional profiling of the chaperone systems interactome in breast cancer using experimental and machine-learning data.

Breast cancer heterogeneity stems from diverse molecular alterations, including proteostasis loss due to chaperone system dysfunction. However, the im...

Attention-based deep learning for immunoglobulin typing from electrophoresis and laboratory data.

BACKGROUND: Interpretation of immunotyping results from serum protein electrophoresis (SPE) remains labor-intensive and subject to inter-observer vari...

Machine learning pipeline for automated segmentation and classification of complicated cystic renal masses on MRI: comparison with radiologists' assessments.

PURPOSE: To develop and validate a machine learning (ML)-based pipeline for automated segmentation and classification of complicated cystic renal mass...

C-peptide Index under Non-fasting Conditions Can Predict the Likelihood of Future Insulin Therapy in Outpatients with Type 2 Diabetes Mellitus Not Receiving Insulin at Baseline.

Objective This study aimed to develop a clinical model in which the C-peptide index (CPI) under non-fasting conditions can predict future insulin ther...

Direction-Aware convolution for airway tubular feature enhancement network.

Manual annotation of airway regions in computed tomography images is a time-consuming and expertise-dependent task. Automatic airway segmentation is t...

Learning curve for fenestrated-branched endovascular aortic repair using machine learning: A prospective national multicenter registry study.

OBJECTIVE: Fenestrated-branched endovascular aortic repair (F-BEVAR) is a complex procedure that requires significant experience and advanced technica...

Molecular pathways of kidney development and their applications to clinical research.

Congenital anomalies of the kidney and urinary tract (CAKUT) are the major cause of childhood chronic kidney disease and an antecedent cause of adult-...

Unsupervised machine learning for identifying morphological phenotypes in abdominal aortic aneurysms using fully automated volume-segmented imaging: a multicentre cohort study.

AIMS: Thrombo- and microembolic complications following abdominal aortic aneurysm (AAA) repair are hypothesized to be associated with wall thrombus bu...

Diverse combination of factors associated with the development of diabetic kidney disease among data-driven diabetes subtypes: analysis of the J-DREAMS registry.

AIMS/HYPOTHESIS: Available methods for predicting the onset and progression of diabetic kidney disease (DKD) and end-stage kidney disease (ESKD) are n...

Deep learning-based perfusion quantification and large vessel exclusion for renal multi-TI arterial spin labelling MRI.

The multi-TI flow-sensitive alternating inversion recovery sequence is a common ASL technique for probing renal perfusion. However, traditional method...