Urology

Machine learning models to predict the recurrence of common bile duct stones after ERCP.

OBJECTIVE: To construct predictive models for the recurrence of common bile duct stones (CBDS) following endoscopic retrograde cholangiopancreatography (ERCP). METHODS: This retrospective study analyzed data from 1,130 patients who were randomly divided into a training set (70%) and a test set (30%). Feature selection was performed using the boruta algorithm and multivariable logistic regression (...

Deep learning applications in cancer treatment Prediction: Comprehensive research foundation for systematic review and Meta-Analysis.

Deep learning has rapidly emerged as a transformative technology in oncology, offering new capabilities in treatment response prediction and personalized cancer care. This systematic review and meta-analysis aim to evaluate the predictive performance, methodological quality, and clinical implementation of deep learning models for cancer treatment outcomes. A comprehensive search across ten databas...

Regulating Innovation: Why the European Union Artificial Intelligence Act Matters for Urologists-Recommendations from the EAU AI Working Group.

The key challenge for artificial intelligence (AI) in urology is no longer technical development alone, but safe translation into regulated clinical u...

Computational pathology model to predict recurrence-free survival in NMPUC patients on BCG-therapy.

Urothelial carcinoma, predominantly appearing as non-muscle-invasive papillary urothelial carcinoma (NMIPUC), exhibits wide clinical variability. Accu...

A lightweight ResNet50V2-ECA model for renal cell carcinoma grading: efficiency, calibration, and state-of-the-art performance.

Accurate International Society of Urological Pathology (ISUP)-grade classification of renal cell carcinoma (RCC) is challenging due to subtle histopat...

Physician adoption patterns of AI-driven clinical decision support systems in urinary tract infection management.

In 2021, Maccabi Healthcare Services (MHS) introduced "UTI Smart-Set" (UTIS), an AI-driven decision support system (DSS) based on a machine-learning (...

A machine learning model integrating two-dimensional ultrasound radiomics and clinical parameters for differentiating membranous nephropathy from IgA nephropathy.

BACKGROUND: Membranous nephropathy (MN) and IgA nephropathy (IgAN) are the two most common primary glomerular diseases in China, with distinct pathoph...

Transforming hemodialysis care: a tripartite collaboration model among medical staff, AI agents, and robots.

Hemodialysis demand is rising as populations age and the chronic kidney disease burden increases, yet dialysis units face persistent workforce constra...

Predicting long-term allograft outcomes in kidney transplant recipients using a machine learning approach: a 5-year retrospective cohort study.

Long-term outcomes of kidney allografts vary significantly among deceased donor kidney transplant recipients, and current prediction tools struggle to...

Integrating large language models into prostate cancer training: evidence from comparative benchmarking and a pilot randomized trial.

BACKGROUND: Large language models (LLMs) are increasingly explored as tools for medical education. However, evidence remains limited regarding their p...

Machine learning for predicting liver metastasis in colorectal cancer.

AIM: To evaluate the performance of machine learning models in predicting liver metastasis in colorectal cancer (CRC) patients using the SEER database...

Identifying PLAU as a shared pathogenic gene in type 2 diabetes and bladder urothelial carcinoma through integrated transcriptomic analysis and machine learning for diagnostic and therapeutic value.

Type 2 diabetes mellitus (T2DM) and bladder urothelial carcinoma (BLCA) are two kinds of diseases that seriously threaten human health. Their pathogen...

Multi-Omics Integration Reveals the Genetic Mechanisms of Periodontitis and Predicts Therapeutic Drugs.

BACKGROUND: Periodontitis is a chronic inflammatory disease driven by host immune dysregulation. However, the specific genetic regulatory mechanisms u...

Preoperative Predictive Modeling of Recurrent Graft Failure: Development and Validation of a 12-Month Prognostic Tool in Kidney Transplant Recipients.

BACKGROUND: Kidney transplantation is among the most effective treatments for end-stage renal disease. However, kidney transplant (KT) recipients rema...

Computer-Interpretable Domain Knowledge for Drug-Induced Acute Kidney Injury: a Knowledge Graph Approach.

In this study, a Knowledge Graph (KG) for Drug-Induced Acute Kidney Injury (DAKI) was developed to provide structured and standardized knowledge about...

Deep-learning-based artificial intelligence approaches for grading and progression prediction of clear cell renal cell carcinoma.

The grading classification of clear cell renal cell carcinoma (CCRCC) is a crucial prognostic factor. However, manual observation can lead to subjecti...

Dynamic urinary proteomics integrates single-cell and spatial transcriptomics to reveal tumour microenvironment and predict immunotherapy response in biliary tract cancer.

BACKGROUND: Most patients with biliary tract cancer (BTC) do not derive durable clinical benefit (DCB) from immune checkpoint inhibitors (ICIs), under...

Multiscale Prediction of Tumor Micronecrosis and Progression in Clear Cell Renal Cell Carcinoma Based on Artificial Intelligence: A Multicenter Cohort Study.

RATIONALE AND OBJECTIVES: Tumor micronecrosis (TM) represents a critical pathological feature influencing the prognosis and surgical management of cle...

mFLIP: metabolic flux interval prediction.

BACKGROUND: Understanding cellular metabolism often involves an accurate estimation of metabolic fluxes-the rates at which metabolites are converted i...

Development and external validation of a machine learning model for predicting post-CABG acute kidney injury: insights from MIMIC database and real-world cardiac surgery cohort.

OBJECTIVE: Acute kidney injury (AKI) is a severe complication following coronary artery bypass grafting(CABG) While machine learning models trained on...