Urology

[Driver gene mutations predicted by pathology-foundation-model and their clinical associations in clear cell renal cell carcinoma].

Objective: To evaluate the performance of a deep learning framework based on the PathOrchestra pathology foundation model for predicting key driver gene mutations (VHL, PBRM1, BAP1, and SETD2) in clear cell renal cell carcinoma (ccRCC), and to analyze the associations of these mutations with clinicopathological characteristics and prognosis using whole-exome sequencing data. Methods: Whole-slide i...

Deep learning models for acute kidney injury prediction: multi-center external validation and evaluation under simulated continuous monitoring conditions.

Acute kidney injury (AKI) is a common hospital complication with substantial morbidity and mortality. Deep learning models for AKI prediction show strong development-cohort performance, but single-point evaluation fails to capture behaviour under continuous monitoring. We conducted a multi-centre retrospective study using electronic health records from three cohorts (n = 157,323 admissions): Natio...

Machine learning-based integration of transcriptome and digital pathology for predicting chemoresistance in muscle-invasive bladder cancer.

Muscle-invasive bladder cancer (MIBC) presents with variable clinical and pathological features, leading to inconsistent responses to standard treatme...

Prostate Cancer Detection on Micro-Ultrasound Raw Data Using a Deep Learning Neural Network.

BACKGROUND: Micro-ultrasound (micro-US) is a clinically available novel high-resolution imaging technology for guiding prostate biopsies. However, cli...

Machine Learning-Based Prediction Model for Infectious Complications in Trauma and Its Association With In-Hospital Mortality.

BACKGROUND: Infectious complications, such as sepsis or catheter-related infections, are common and serious sequelae after trauma. Despite their clini...

Autoencoder/RandomForest-TabPFN for Cross-Cancer Metabolomics: Prostate and Breast Cancer Diagnosis Using Paper Spray and Ion Mobility-Mass Spectrometry Techniques.

Accurate and rapid disease diagnosis, particularly in prostate cancer (PC) and breast cancer (BC), is critical for early intervention and improved pat...

Commercial AI for CT lung cancer screening: product capabilities, coverage of nodule management tasks and supporting evidence.

OBJECTIVES: To characterize the capabilities of CE-marked AI products for lung nodule analysis in lung cancer screening (LCS), quantify their coverage...

Quantifying ergonomic challenges for urologists operating flexible ureteroscopes through artificial intelligence-based posture estimation.

PURPOSE: The ergonomic challenges faced by surgeons during flexible ureteroscopy have yet to be thoroughly evaluated using objective methods. However,...

Integrating single-cell multi-omics and machine learning to reveal triaptosis heterogeneity in clear cell renal cell carcinoma.

Triaptosis, an emerging form of cell death, remains poorly characterized in terms of its heterogeneity within clear cell renal cell carcinoma (ccRCC)....

The sleeping kidney: microRNA expression profiling of kidney tissue during daily torpor in mice.

MicroRNA (miRNA), a class of short non-coding RNA, serves as a metabolically cheap and fast acting mechanism to inhibit translation of complementary m...

Risk Stratification for Breast Cancer Screening: AJR Expert Panel Narrative Review.

Early detection of breast cancer reduces mortality and is influenced by screening strategies. The balance of benefits and harms within any screening p...

Alignment between AI clinical decision tools and multidisciplinary tumor board decisions in prostate cancer.

PURPOSE: This study aimed to evaluate the concordance between treatment recommendations generated by LLMs and decisions made by a multidisciplinary ur...

Evaluating the Potential Impact of AI on Urinary Tract Infection Diagnosis in the Emergency Department Across Demographic Groups: Retrospective Cohort Study.

BACKGROUND: Urinary tract infection (UTI) is a common emergency department (ED) presentation but can be challenging to diagnose; both overdiagnosis an...

Machine learning in bleeding risk assessment for low-molecular-weight heparin or fondaparinux: a predictive model study.

BACKGROUD:  No universally accepted model exists for predicting bleeding risk in patients receiving low-molecular-weight heparin or fondaparinux. OBJE...

Research progress on biomarkers for acute kidney injury in children.

Pediatric acute kidney injury (AKI) often presents insidiously and progresses rapidly. Traditional diagnostic criteria based on serum creatinine and u...

Personalized risk prediction of financial toxicity in patients with cancer: an interpretable machine learning study.

BACKGROUND: Financial toxicity (FT), the economic stress from medical care, is common among people with cancer and is associated with worse health out...

Diuretic Ultrasound Evaluation of Surgical Necessity in Pediatric Ureteropelvic Junction Obstruction.

RATIONALE AND OBJECTIVES: This study aimed to develop an interpretable machine learning (ML) model using diuretic ultrasonography to predict the neces...

Global research trends in artificial kidneys (2014-2024): A bibliometric and visualization study.

When renal function is lost following resection of renal tumors, in the setting of end-stage kidney disease, or after traumatic nephrectomy, kidney tr...

Machine learning for the prediction of three-year survival in locally advanced breast cancer patients receiving neoadjuvant chemotherapy using quantitative ultrasound imaging.

Locally Advanced Breast Cancer (LABC) is a serious type of cancer with a poor prognosis despite advances in cancer treatment. As the disease is often ...

Comprehensive evaluation of cross cancer generalization in histopathology segmentation models across 21 tumor types.

BACKGROUND: Artificial intelligence has significantly advanced computational pathology by enabling high-resolution, clinical-grade tumor segmentation ...