Urology

The diagnostic performance of machine learning based detection of urinary tract stones: a systematic review and meta-analysis.

BACKGROUND: Urolithiasis is a prevalent urological condition, and Non-Contrast Computed Tomography (NCCT) is the gold standard for diagnosis. In recent years, there has been growing interest in investigating machine learning (ML)- based detection of urolithiasis and the wider potential of AI in urology. PURPOSE: To synthesise the diagnostic accuracy of ML-based UTS detection on NCCT and in externa...

Development and validation of a nomogram for predicting advanced chronic kidney disease.

The objective of this study was to develop and validate a nomogram for predicting advanced chronic kidney disease (CKD) through the utilization of routine clinical parameters. To achieve this, we integrated internal and external datasets, employed LASSO and logistic regression to identify independent predictors of advanced CKD for nomogram construction, conducted Bootstrap validation on the traini...

Association Between Environmental Chemicals Phthalates and Stroke Among the US Adults: A Cross-Sectional Study.

Stroke has a multifactorial etiology, and phthalates, as widely used environmental chemicals, may play an underexplored role in cerebrovascular health...

Application of transformer-enhanced convolutional neural network: multicenter MRI assessment of muscle invasion in bladder cancer.

OBJECTIVE: Accurate preoperative assessment of muscle invasion in bladder cancer (BCa) guides therapy selection. However, MRI interpretation varies ac...

A deep learning-based multimodal model with automated body composition analysis predicts prognosis in advanced clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is an aggressive malignancy with a high risk of postoperative recurrence. Body composition has emerged as a pr...

Development and validation of an interpretable machine learning model for predicting urinary incontinence at 6 months after robot-assisted radical prostatectomy.

Multimorbidity and major adverse cardiovascular events in antipsychotic users: Time-to-event prediction by explainable machine learning.

Antipsychotic treatment is associated with higher risk of major adverse cardiovascular events (MACEs), and risk may vary by multimorbidity and concomi...

Cluster-defined gout subtypes exhibit divergent chronic kidney disease trajectories: a machine learning approach in a Chinese prospective cohort.

OBJECTIVE: To systematically evaluate the contribution of clinical heterogeneity to chronic kidney disease (CKD) progression in gout patients using da...

Determination of Trichomonas vaginalis, certain bacterial and fungal positivity, and risk factors in female patients with urinary and genital complaints.

Deciphering stone mining-induced hazardous heavy metal contamination in agricultural soils using source attribution, health-dietary risk analysis, and machine learning-driven insights.

Mining activities significantly contribute to the release of hazardous heavy metals (HHMs) into agricultural soils, especially where stone extraction ...

Periprocedural evaluation of patients with BAV stenosis undergoing TAVR: a machine learning-based study.

BACKGROUND: Transcatheter aortic valve replacement (TAVR) has increasingly emerged as one of the primary treatments for patients with severe bicuspid ...

Clinical and immunological significance of FOXO1 as a biomarker of improved response to immune checkpoint plus tyrosine kinase inhibitor therapy in metastatic renal cell carcinoma.

BACKGROUND: Predicting response to immune checkpoint inhibitor plus tyrosine kinase inhibitor (IO+TKI) therapy in metastatic renal cell carcinoma (mRC...

Towards Clinical Integration of Deep Learning-Based Classification of Urinary Sediment Particles from Digital Microscopy Images: A Prospective Study.

BACKGROUND: Urinalysis is a standard clinical test that includes the microscopic examination of urinary sediment to identify formed elements. Manual e...

European Society of Urogenital Radiology (ESUR) perspectives on the role of prostate MRI in active surveillance.

MRI has a central role in the diagnosis and management of prostate cancer, including active surveillance (AS) of low- and favourable intermediate-risk...

Diagnostic assessment of artificial intelligence reconstruction on accelerated prostate MRI: a retrospective, paired, multi-reader multi-case study.

OBJECTIVES: To determine whether AI-reconstructed prostate MRI at reduced acquisition times maintains prostate cancer (PCa) detection performance comp...

Multi-omics identification of a programmed cell death-related signature and potential target P4HB for bladder cancer based on a 101-combination machine learning and experimental validation.

Dosimetry Analysis of 177Lu-PSMA-I&T in Patients with Low-Volume Oligometastatic Hormone-Sensitive Prostate Cancer: A Secondary Analysis of the LUNAR Trial.

The phase 2 LUNAR trial randomized (1:1) patients with oligorecurrent hormone-sensitive prostate cancer to neoadjuvant [177Lu]Lu-PSMA-I&T (2 cycles, 6...

Potential of large language models for rapid clinical information support: evidence from acute kidney injury knowledge testing.

Large language models (LLMs) are increasingly applied in clinical contexts; however, to our knowledge, their performance in acute kidney injury (AKI) ...

Generative Artificial Intelligence for Medical Summarization in Prostate Cancer: Comparative Evaluation by Physicians and Patient Advocates-A Pilot Study.

PURPOSE: The exponential growth of scientific publications presents increasing challenges for clinicians and patients seeking to access up-to-date med...

Real-world text-only inference of PI-RADS v2.1 from prostate MRI reports using large language models: a lesion-level, zone-aware study.

PURPOSE: To evaluate the feasibility and limitations of real-world, text-only inference of PI-RADS v2.1 categories from prostate MRI reports using lar...