Urology

An evaluation of artificial intelligence assisted prostate biopsy reporting in the Articulate Pro study.

Prospective evidence on clinical utility of AI in histopathology is limited. We conducted a prospective study across three National Health Service specialist centres in England to evaluate a commercially available AI system for assistance in prostate biopsy reporting. Of 1613 cases, 1049 were reported with AI-assistance. Endpoints evaluated diagnostic impact, clinical impact and workflow. Staged A...

Representation of Ordinal Features: Supervised Embeddings in the Survival Prediction of Prostate Cancer Patients.

Prostate cancer (PCa) is the most common cancer in men. Treatment decisions for PCa consider factors like age, tumor stage, and grade, along with specific prostate-related factors such as prostate-specific antigen (PSA) level and Gleason score. Most of these features are ordinal, meaning they have an inherent order of their categorical levels. Machine learning (ML) approaches are increasingly used...

Identification of Cervical Cancer Biomarkers Using Gene Co-Expression Networks and Machine Learning Methods.

Cervical cancer (CC) causes significant mortality due to late diagnosis and limited understanding of its molecular drivers. The complex gene co-expres...

Human-AI Interaction in Kidney Transplant Decision Support Systems: Qualitative Study of Patient and Support Person Expectations.

BACKGROUND: Artificial intelligence (AI) is increasingly applied in medicine, including clinical decision-making. AI-based decision support systems (D...

ATBiGRU: an interpretable temporal model for in-hospital mortality prediction in acute kidney injury.

Acute kidney injury (AKI) is associated with high mortality and healthcare burden, particularly in developing countries. Existing machine learning mod...

Integrating network pharmacology, molecular docking, machine learning, and experimental validation: puerarin improves sepsis-induced acute kidney injury via the Sirt1-Nrf2-HO-1 pathway.

Acute kidney injury (AKI) associated with sepsis has a high clinical mortality rate, and there is a lack of effective therapeutic targets; uncontrolle...

[Artificial intelligence in urological surgery].

BACKGROUND: The integration of artificial intelligence (AI) into urological robotic surgery is currently in a dynamic phase of development and validat...

Development and validation of a machine learning-based risk prediction model for hyperkalemia in patients with chronic kidney disease.

To develop an optimal predictive model for hyperkalemia in patients with chronic kidney disease (CKD). Clinical data of CKD patients were collected fr...

Probabilistic cancer risk assessment from heavy metal exposure in iranian rice and pasta: a novel hybrid framework integrating INAA, ICP-AES, and ensemble machine learning.

This study investigates cancer risk from heavy metal exposure in rice and pasta using experimental data and machine learning approaches, based on 19 e...

Life's Essential 8-Defined Cardiovascular Health and Mortality Risk in Cardiovascular-Kidney-Metabolic Health Stages 0-4: A Cohort Study.

BACKGROUND: Cardiovascular-kidney-metabolic (CKM) syndrome represents a primary contributor to global morbidity and mortality. Despite the Life's Esse...

Integrating machine learning and structural analysis to decipher benzo[a]pyrene-induced bladder cancer networks.

Benzo[a]pyrene (BaP), a polycyclic aromatic hydrocarbon from tobacco smoke, exhaust, and pollutants, is linked to bladder cancer (BLCA). We systematic...

Molecular design of electrolyte additives for aqueous zinc-ion batteries via reinforcement learning and quantum chemistry calculations.

The use of electrolyte additives is regarded as a cost-effective strategy to regulate the components of aqueous zinc-ion batteries (AZIBs) and to impr...

Eigen-guided transformer: A data-driven approach for chronic kidney disease forecasting.

Accurate prediction of Chronic Kidney Disease (CKD) development is essential for prompt therapeutic intervention; nevertheless, it is difficult due to...

Performance of a self-attention-based model in the task of differentiating clear cell renal cell carcinoma from other renal tumors: variable Vision Transformer (vViT).

OBJECTIVES: To examine the performance of the variable Vision Transformer (vViT) in comparison with that of convolutional neural networks (CNNs) in th...

An evidence-based nursing management app prototype for adult urology day surgery: an AI-driven development pathway.

OBJECTIVES: The nursing management of adult urology patients in day surgical settings has undergone rapid development. This study aimed to (1) retriev...

Dynamic Bayesian networks to predict loss of kidney function: a cross-institution use case in a large cohort with or at-risk of CKD.

BACKGROUND: Substantial loss of kidney function, measured as ≥40% decline in estimated glomerular filtration rate (eGFR) within a 2-year period, is as...

Research on biomarkers screening for urinary tract infection and the prediction model based on machine learning algorithms.

BACKGROUND: Urinary tract infections (UTIs) affect over 1.5 billion people annually, yet systematic screening of cytokine combinations as biomarkers r...

Relationship between the diagnostic probability of chronic kidney disease calculated using artificial intelligence-enhanced electrocardiography and the incidence of cardiovascular events.

AIMS: A low estimated glomerular filtration rate (eGFR) is the primary diagnostic criterion for chronic kidney disease (CKD), a known risk factor for ...

Deep Learning Analysis of Indocyanine Green Fluoroscopy of Ureters in Robotic Cystectomy: Toward Reducing Ureteroenteric Strictures.

OBJECTIVES: To develop a deep learning method to quantify ureter perfusion during indocyanine green (ICG) fluoroscopy in robot-assisted radical cystec...

Accuracy and precision of automated kidney stone detection on CT.

BACKGROUND: While computed tomography (CT) is the preferred imaging modality for kidney stone detection and measurement, quantifying the accuracy and ...