Urology

Early cancer detection using deep learning and medical imaging: A survey.

Cancer, characterized by the uncontrolled division of abnormal cells that harm body tissues, necessitates early detection for effective treatment. Medical imaging is crucial for identifying various cancers, yet its manual interpretation by radiologists is often subjective, labour-intensive, and time-consuming. Consequently, there is a critical need for an automated decision-making process to enhan...

Assessment of a fully-automated diagnostic AI software in prostate MRI: Clinical evaluation and histopathological correlation.

OBJECTIVE: This study aims to evaluate the diagnostic performance of a commercial, fully-automated, artificial intelligence (AI) driven software tool in identifying and grading prostate lesions in prostate MRI, using histopathological findings as the reference standard, while contextualizing its performance within the framework of PI-RADS v2.1 criteria.

Machine learning and explainable artificial intelligence to predict pathologic stage in men with localized prostate cancer.

BACKGROUND: Though several nomograms exist, machine learning (ML) approaches might improve prediction of pathologic stage in patients with prostate ca...

A novel approach to the cause of death identification-multi-strategy integration of multi-organ FTIR spectroscopy information using machine learning.

Identifying the cause of death has always been a major focus and challenge in forensic practice and research. Traditional techniques for determining t...

Triple and quadruple optimization for feature selection in cancer biomarker discovery.

The proliferation of omics data has advanced cancer biomarker discovery but often falls short in external validation, mainly due to a narrow focus on ...

Exploring the Efficacy of Artificial Intelligence: A Comprehensive Analysis of CHAT-GPT's Accuracy and Completeness in Addressing Urinary Incontinence Queries.

BACKGROUND: Artificial intelligence models are increasingly gaining popularity among patients and healthcare professionals. While it is impossible to ...

Features selection in a predictive model for cardiac surgery-associated acute kidney injury.

BackgroundCardiac surgery-associated acute kidney injury (CSA-AKI) is related to increased morbidity and mortality. However, limited studies have expl...

Self-Supervised Learning for Feature Extraction from Glomerular Images and Disease Classification with Minimal Annotations.

BACKGROUND: Deep learning has great potential in digital kidney pathology. However, its effectiveness depends heavily on the availability of extensive...

Predicting Response to Intravesical Bacillus Calmette-Guérin in High-Risk Nonmuscle-Invasive Bladder Cancer Using an Artificial Intelligence-Powered Pathology Assay: Development and Validation in an International 12-Center Cohort.

PURPOSE: There are few markers to identify those likely to recur or progress after treatment with intravesical bacillus Calmette-Guérin (BCG). We deve...

Rapid diagnosis and recurrence prediction of choledocholithiasis disease using raw bile with machine learning assisted SERS.

Surface-enhanced Raman spectroscopy (SERS) analysis based on body fluids has been widely applied in disease diagnose. Choledocholithiasis is a widespr...

Serum Potassium Monitoring Using AI-Enabled Smartwatch Electrocardiograms.

BACKGROUND: Hyperkalemia, characterized by elevated serum potassium levels, heightens the risk of sudden cardiac death, particularly increasing risk f...

Evaluation of the accuracy of automated segmentation based on deep learning for prostate cancer patients.

PURPOSE: This study evaluated the accuracy of a commercial deep learning (DL)-based algorithm for segmenting the prostate, seminal vesicles (SV), and ...

Development and validation of the PHM-CPA model to predict in-hospital mortality for cirrhotic patients with acute kidney injury.

BACKGROUND: The presence of acute kidney injury (AKI) significantly increases in-hospital mortality risk for cirrhotic patients. Early prognosis predi...

Clinicopathological features for the prediction of immunosuppressive treatment responses in sarcoidosis-related kidney involvement: a single-center retrospective study.

BACKGROUND/AIM: Sarcoidosis is a multisystem disorder that affects many organs, including the kidneys. This single-center retrospective study investig...

AI-PEDURO - Artificial intelligence in pediatric urology: Protocol for a living scoping review and online repository.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) methods are increasingly being applied in pediatric urology across a growing number...

MixUNETR: A U-shaped network based on W-MSA and depth-wise convolution with channel and spatial interactions for zonal prostate segmentation in MRI.

Magnetic resonance imaging (MRI) plays a pivotal role in diagnosing and staging prostate cancer. Precise delineation of the peripheral zone (PZ) and t...

Stereotactic ablative radiotherapy (SABR) for pelvic nodal oligorecurrence in prostate cancer.

BACKGROUND: This study evaluated the clinical outcomes of stereotactic ablative radiotherapy (SABR) in the treatment of oligometastatic pelvic node pr...

Rapid and noninvasive estimation of human arsenic exposure based on 4-photo-set of the hand and foot photos through artificial intelligence.

Chronic exposure to arsenic is linked to the development of cancers in the skin, lungs, and bladder. Arsenic exposure manifests as variegated pigmenta...

Histopathology-driven prostate cancer identification: A VBIR approach with CLAHE and GLCM insights.

Efficient extraction and analysis of histopathological images are crucial for accurate medical diagnoses, particularly for prostate cancer. This resea...

Machine learning models for discriminating clinically significant from clinically insignificant prostate cancer using bi-parametric magnetic resonance imaging.

PURPOSE: This study aims to demonstrate the performance of machine learning algorithms to distinguish clinically significant prostate cancer (csPCa) f...