Urology

Training With Local Data Remains Important for Deep Learning MRI Prostate Cancer Detection.

OBJECTIVES: Domain shift has been shown to have a major detrimental effect on AI model performance however prior studies on domain shift for MRI prostate cancer segmentation have been limited to small, or heterogenous cohorts. Our objective was to assess whether prostate cancer segmentation models trained on local MRI data continue to outperform those trained on external data with cohorts exceedin...

Prostate MR image segmentation using a multi-stage network approach.

Prostate cancer (PCa) remains one of the most prevalent cancers among men, with over 1.4 million new cases and 375,304 deaths reported globally in 2020. Current diagnostic approaches, such as prostate-specific antigen (PSA) testing and trans-rectal ultrasound (TRUS)-guided biopsies, are often Limited by low specificity and accuracy. This study addresses these Limitations by leveraging deep learnin...

Optimizing guideline-directed medical therapy in chronic kidney disease and type 2 diabetes mellitus patients in primary care.

BACKGROUND: Despite KDIGO (Kidney Disease: Improving Global Outcomes) recommendations for renin-angiotensin-aldosterone system inhibitors (RAASi's) an...

Multi-regional Multiparametric Deep Learning Radiomics for Diagnosis of Clinically Significant Prostate Cancer.

Non-invasive and precise identification of clinically significant prostate cancer (csPCa) is essential for the management of prostatic diseases. Our s...

Bias in deep learning-based image quality assessments of T2-weighted imaging in prostate MRI.

OBJECTIVES: To determine whether deep learning (DL)-based image quality (IQ) assessment of T2-weighted images (T2WI) could be biased by the presence o...

Application of machine learning and deep learning in metabolic dysfunction-associated steatotic liver disease: a systematic review and meta-analysis.

INTRODUCTION: Metabolic dysfunction-associated steatotic liver disease (MASLD) can progress to metabolic dysfunction-associated steatohepatitis (MASH)...

Development and validation of a natural language processing system to assess quality of physician communication in prostate cancer consultations.

BACKGROUND: AUA guidelines for shared decision making (SDM) in prostate cancer recommend discussion of five content areas in consultations: (1) cancer...

Utilizing a combined approach of machine learning and structure-based drug design principles to identify potential hits targeting SphK1.

Sphingosine kinase (SphK1) is acrucial enzyme that aids in the processing of sphingolipids by adding a phosphate group to sphingosine, converting it i...

Machine learning models to predict postoperative incontinence after endoscopic enucleation of the prostate for benign prostatic hyperplasia: An EAU-Endourology study.

BACKGROUND: Machine learning (ML) and artificial intelligence (AI) have demonstrated powerful functionality in the healthcare setting thus far. We aim...

Deep learning-based quantitative assessment of renal chronicity indices in lupus nephritis.

Renal chronicity indices (CI) have been identified as strong predictors of long-term outcomes in lupus nephritis (LN) patients. However, assessment by...

Determining the Survival Impact and Cost-Effectiveness of Multi-Gene Panel Sequencing in Metastatic Colorectal Cancer With Super Learning Approaches.

OBJECTIVE: To determine the effectiveness and cost-effectiveness of multi-gene panel sequencing compared to single-gene KRAS testing for metastatic co...

Predicting and Optimizing Synergistic Drug Combinations for Breast Cancer Treatment Using Machine Learning.

OBJECTIVES: The study aims to identify highly synergistic drug combinations for breast cancer treatment using machine learning models. The primary obj...

Portrayal of Cancer Patients in the Era of AI: A Content Analysis of Images Produced by Generative AI Tools.

This study sought to characterize images of cancer patients generated by Artificial Intelligence (AI) text-to-image tools, and assess whether images d...

Repeatability and Reproducibility of Artificial Imaging-based Digital Pathology for the Evaluation of Liver Fibrosis.

BACKGROUND & AIMS: Fibrosis stage is a key determinant of outcomes in metabolic dysfunction-associated steatohepatitis (MASH). Assessment of fibrosis ...

Machine learning-based MRI imaging for prostate cancer diagnosis: systematic review and meta-analysis.

OBJECTIVE: This study aims to evaluate the diagnostic value of machine learning-based MRI imaging in differentiating benign and malignant prostate can...

Tissue perfusion pressure: A novel hemodynamic measure to assess risk of acute kidney injury after cardiac surgery.

BACKGROUND: Optimal hemodynamic targets for preventing acute kidney injury (AKI) have remained elusive. We hypothesized that lower tissue perfusion pr...

Impact of three-dimensional prostate models during robot-assisted radical prostatectomy on surgical margins and functional outcomes.

BACKGROUND: Robot-assisted radical prostatectomy (RARP) is the standard surgical procedure for the treatment of prostate cancer. RARP requires a trade...

Machine learning-based prediction of stone-free rate after retrograde intrarenal surgery for lower pole renal stones.

BACKGROUND: Lower pole renal stones (LPS) present unique challenges for retrograde intrarenal surgery (RIRS) due to unfavorable anatomical features, o...

AI-driven robotic surgery in oncology: advancing precision, personalization, and patient outcomes.

Artificial intelligence (AI) integrated with robotic systems is transforming oncologic surgery by significantly improving precision, safety, and perso...

Identification and validation of epithelial‑mesenchymal transition‑related genes for diabetic nephropathy by WGCNA and machine learning.

Diabetic nephropathy (DN) is the main cause of end‑stage renal disease, with epithelial‑mesenchymal transition (EMT) serving a key role in its initiat...