Urology

Leveraging Clinical Text and Class Conditioning for 3D Prostate MRI Generation.

OBJECTIVE: Latent diffusion models (LDM) could alleviate data scarcity challenges affecting machine learning development for medical imaging. However, medical LDM strategies typically rely on short-prompt text encoders, nonmedical LDMs, or large data volumes. These strategies can limit performance and scientific accessibility. We propose a novel LDM conditioning approach to address these limitatio...

3D MR Fingerprinting for Quantitative Bladder Wall T1, T2, and M0 Mapping in Healthy Subjects at 1.5 T and 3 T.

Current diagnostic tests for lower urinary tract symptoms (LUTS) do not assess tissue-level alterations in the bladder wall. This study assesses the feasibility of 3D magnetic resonance fingerprinting (MRF) for simultaneous T1, T2, and proton density (M0) mapping of the bladder wall in healthy subjects at 1.5 T and 3 T. A 3D MRF acquisition was combined with a deep image prior reconstruction to ac...

Deep learning auto-contouring of target and organs-at-risk on post-catheter implant CT images for prostate HDR brachytherapy.

BACKGROUND: Accurate delineation of the prostate and surrounding organs-at-risk (OARs) is essential for HDR prostate brachytherapy. Manual contouring ...

Multi-fidelity Monte Carlo fusion for BNCT dose calculation using a dual-branch network.

BACKGROUND: Boron neutron capture therapy (BNCT) is a binary radiotherapy that selectively kills tumor cells. Its clinical implementation relies on Mo...

Evaluation of AI-based segmentation and synthetic CT generation for MRI-only prostate radiotherapy planning.

BACKGROUND: There is an increasing use of magnetic resonance imaging (MRI) for segmentation of target volumes and organs at risk due to superior soft ...

Effect of a Machine Learning Algorithm to Guide Goal-Directed Therapy After Cardiac Surgery.

BACKGROUND: Goal-directed therapy allows clinicians to optimize perfusion and volume status in patients postoperatively. OBJECTIVE: To evaluate the ef...

Machine learning-based classification model to distinguish tumor and tumor-free samples using synthetic plasma proteomic dataset.

BACKGROUND: Tumor-Specific Peptides (TSPs) and Tumor-associated Overexpressed Proteins (TOPs) are promising biomarkers for cancer diagnosis and monito...

The diagnostic accuracy of left ventricular ejection fraction assessment between visual and artificial intelligence-based algorithms on bedside ultrasound.

BACKGROUND: Focused cardiac ultrasound (FoCUS) has become the standard of care for bedside assessments of cardiac function. With the integration of ar...

Kidney stones composition prediction using artificial intelligence (KiSCAI) study.

OBJECTIVES: To develop and validate explainable machine learning (ML) models predicting urinary stone composition from routinely available clinical va...

Machine learning for predicting stone-free rate after retrograde intrarenal surgery: Comparison with the T.O.HO. score.

Urolithiasis remains a significant clinical burden, and accurately predicting stone-free status after retrograde intrarenal surgery (RIRS) is essentia...

Machine Learning-Driven Risk Clusters for Adverse Outcomes in Patients Hospitalized with Acute Heart Failure: Using a Retrospective Cohort Data.

AIM: Heart failure (HF) poses a growing public health burden, yet conventional risk stratification models fail to capture the multidimensional complex...

Identification of comorbidity-based clusters and mortality risk in hospitalised patients with diabetes mellitus in Karachi, Pakistan: a machine learning analysis.

BACKGROUND: Diabetes has reached epidemic proportions in Pakistan. This study applied machine learning (ML) techniques to identify comorbidity-based a...

Comparison and interpretability of machine learning algorithms to predict survival of patients with prostate cancer.

OBJECTIVE: To evaluate the performance and interpretability of multiple ML algorithms for survival prediction in prostate cancer (CaP) and to assess w...

Apparent diffusion coefficient radiomics for differentiating benign and malignant PI-RADS 3-5 prostate lesions: external validation.

PURPOSE: To develop and externally validate a simplified apparent diffusion coefficient (ADC)-based MRI radiomics model for differentiating benign and...

Multi-task Deep Learning via UNETR for PSMA PET/CT Image for Prostate Cancer: Simultaneous Assessment of Prostate Cancer Disease Burden, Treatment Selection and Survival Outcomes.

Prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) provides rich molecular imaging across the prostate ...

Prognostic Value of Artificial Intelligence ECG-Derived Diastolic Function in Surgical Aortic Valve Replacement.

BACKGROUND: Diastolic dysfunction is common in patients with aortic stenosis and may influence outcomes following surgical aortic valve replacement. W...

Parkinson's Disease and Retinal Age Gap: A Cross-Sectional Analysis.

BACKGROUND: Deep-learning models are capable of predicting age from retinal scans and the difference between this and chronological age, retinal age g...

Intrarenal pressure monitoring in retrograde intrarenal surgery for high-infectious risk populations.

PURPOSE: To evaluate the feasibility of real-time intrarenal pressure (IRP) monitoring using the LithoVue™ Elite (LVE) ureteroscope and assess postope...

Emerging frontiers and challenges of artificial intelligence in PSMA-PET imaging: pioneering a new chapter in prostate cancer care.

Prostate-specific membrane antigen positron emission tomography (PSMA-PET) has become pivotal in prostate cancer (PCa) management, offering superior s...

Real-Time Dynamic Prediction of Mortality and Renal Replacement Therapy After Cardiac Surgery Using a Time-Series Deep Learning Model.

OBJECTIVE: Predicting postoperative deterioration following cardiac surgery remains challenging. Conventional risk scores rely on static variables and...