Urology

Discovery of an Artificial Intelligence Label Feedback Loop: How the Success of a Clinically Implemented Artificial Intelligence Algorithm Has Created an Unforeseen Challenge to Algorithm Retraining.

BACKGROUND: Artificial intelligence (AI) augmented laboratory tests can improve quality, efficiency, cost-effectiveness, and staff satisfaction. However, the clinical success of these tests can introduce unforeseen challenges for model retraining. This study describes the discovery of an "AI label feedback loop" in a clinically implemented AI-augmented kidney stone composition test. METHODS: An AI...

RETRACTED: Lee et al. Machine-Learning-Based Survival Prediction in Castration-Resistant Prostate Cancer: A Multi-Model Analysis Using a Comprehensive Clinical Dataset. J. Pers. Med. 2025, 15, 432.

The journal retracts the article "Machine-Learning-Based Survival Prediction in Castration-Resistant Prostate Cancer: A Multi-Model Analysis Using a Comprehensive Clinical Dataset" [...].

Evaluating dose distribution in prostate IMRT patients using deep learning: the influence of loss function on model performance.

To estimate the influence of various loss functions on the performance of deep learning (DL) models for dose prediction in intensity-modulated radioth...

Interpretable machine learning reveals phosphorus-to-albumin ratio as a novel predictor of mortality and acute kidney injury in critically ill pancreatitis patients: a multi-center retrospective analysis.

Acute kidney injury (AKI), a common and severe complication of acute pancreatitis (AP), is amenable to early intervention. Phosphorus-to-albumin ratio...

Editorial Comment on: "Enhanced Artificial Intelligence in Bladder Cancer Management: A Comparative Analysis and Optimization Study of Multiple Large Language Models" by Li et al.

EXPRESS: Convolutional Autoencoder for Automated Pre-Processing of Tumor Cell and Tissue Raman Spectra.

Raman spectroscopy (RS) is a label-free, non-destructive optical modality that provides a detailed profile of the molecular composition of a sample. T...

Protocol for the PROSECCA study: a new approach for predicting radiotherapy outcome using artificial intelligence and electronic population-based healthcare data.

INTRODUCTION: Within the UK there are 33 deaths every day from prostate cancer, second only to lung cancer as the most common cause of cancer death in...

Single-cell transcriptomics and machine-learning reveal M1 macrophage-driven progression from minimal change disease to focal segmental glomerulosclerosis.

Minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS) are two key nephrotic syndrome types with significant clinical implications...

Rethinking Cardiopulmonary Bypass Management in The Digital Health Era.

Minimally invasive and robotic cardiac surgery have been developed to reduce surgical trauma, shorten recovery, and improve cosmetic and functional ou...

Predicting Treatment Outcomes in Patients With Psoriatic Arthritis or Axial Spondyloarthritis: An Artificial Intelligence-Driven Approach.

OBJECTIVE: To develop machine learning (ML) models to predict the probability at baseline of achieving low disease activity (LDA) and high health-rela...

Prostate Cancer Characterization Based on Rapid SHG Imaging of Collagen Fiber Combined With Denoising Algorithm.

Second harmonic generation (SHG) imaging technique can specifically image components such as collagen fibers, which provides diagnostic information fo...

Integrated network pharmacology and bioinformatics analysis reveals MME as key target of Notoginsenoside R1 in diabetic nephropathy.

BACKGROUND: Diabetic nephropathy (DN) is a major diabetes complication and a primary cause of end-stage renal failure. Notoginsenoside R1 (NGR1) is kn...

Seeing Is Believing? Exploring Gender Bias in Artificial Intelligence Imagery of Specialty Doctors.

BACKGROUND: In medicine and medical education, women are disproportionately affected by gender bias. Artificial intelligence (AI) is increasingly bein...

Genomics of kidney volumes: one size does not fit all.

To characterize the genetic basis of chronic kidney disease, genome-wide association studies focused on chronic kidney disease-defining traits that we...

Mitigating Disparities in Prostate Cancer Survival Prediction Through Fairness-Aware Machine Learning Models.

PURPOSE: Prediction models can contribute to disparities in care by performing unequally across demographic groups. While fairness-aware methods have ...

Image reconstruction and elongation artifact reduction for a dual-panel dedicated prostate PET scanner.

BACKGROUND: The development of PET scanners dedicated to high temporal and spatial resolution organ-specific imaging is an active research area, motiv...

Comparison of Digital Mammography Plus Full ABUS Review and Digital Mammography Plus Selective ABUS Review Guided by ABUS Artificial Intelligence-Computer-Aided Diagnosis for Breast Cancer Screening.

OBJECTIVE: To compare two breast cancer screening strategies, digital mammography (DM) plus radiologist-interpreted automated breast ultrasound (ABUS)...

The Effectiveness of Artificial Intelligence-Enhanced Interventions for Cancer Patients: A Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: The incidence of cancer continues to increase, and cancer patients still suffer from a range of burdens, leading to decreased quality of l...

Beam angle optimization for radiotherapy using LLMs via reinforcement-learning inspired iterative refinement.

BACKGROUND: Radiotherapy treatment planning (TP) aims to maximize radiation dose delivered to tumors while minimizing exposure to surrounding healthy ...

Machine learning for optimizing mAs in KUB radiography with metal implants.

BACKGROUND AND PURPOSE: Kidney-ureter-bladder (KUB) radiography is a common examination that exposes patients to a higher radiation dose and increased...