Urology

Ethnicity-specific molecular subtypes and a machine-learning risk model in Asian patients with non-muscle-invasive bladder cancer.

Non-muscle-invasive bladder cancer (NMIBC) has high rates of recurrence and progression, yet its molecular landscape across different ethnicities remains poorly defined. We assembled a meta-cohort of 202 Asian NMIBC transcriptomes and identified three novel molecular subtypes (AC0, AC1, AC2) using non-negative matrix factorization clustering. These subtypes, each associated with distinct biologica...

Development and validation of a noninvasive machine learning model using urinary extracellular vesicle physical parameters for prostate cancer diagnosis.

Urinary extracellular vesicles (uEVs) are promising biomarkers for prostate cancer (PCa). Although novel uEV-based biomarkers have advanced early diagnosis, their detection processes remain cumbersome and cost-prohibitive. The physical parameters of uEVs offer untapped predictive potential for PCa, providing a more convenient, rapid, and cost-effective diagnostic approach. This study aims to const...

MSTM-Net: a two-stage prostate cancer segmentation network based on swin-transformer-mamba architecture.

BACKGROUND: Magnetic resonance imaging (MRI) has become a core imaging modality for prostate cancer screening and diagnosis. Accurate and automatic se...

Machine learning models based on magnetic resonance imaging for predicting Lymphovascular Invasion in Invasive Breast Cancer.

OBJECTIVES: Treatment strategies for invasive breast cancer require accurate lymphovascular invasion (LVI) predictions. This study aimed to investigat...

PGFD-YOLO: A dual-modal object detection framework with progressive gated fusion and foreground-guided distillation.

Effective waste classification is essential for improving resource recovery and reducing landfill burden. However, existing methods predominantly rely...

Chronic kidney disease of unknown causes in Mesoamerica. An approach from the Perspective of Medical Geology.

In Mesoamerica, new cases of chronic kidney disease of unknown cause (CKDu) are increasing each year, including those requiring renal replacement ther...

Multimodal deep learning model for AI-based functional prognostic risk stratification in patients undergoing radical nephrectomy.

Making the decision between technically challenging partial nephrectomy (PN) and radical nephrectomy (RN) in patients with complex renal cell carcinom...

Capsid Engineering of Adeno-Associated Viruses for Targeted Gene Therapy in Kidney Diseases.

The global burden of chronic and genetic kidney diseases poses a significant challenge to healthcare systems. Current therapies, including dialysis, t...

Machine Learning Model Using Pre-Cancer Therapy Cardiac Magnetic Resonance Images to Predict Cancer Therapy-Related Cardiac Dysfunction.

BACKGROUND: Predicting risk of cancer therapy-related cardiac dysfunction (CTRCD) remains challenging. OBJECTIVES: The purpose of this study was to as...

Displaced minds: ecologies of depression.

This issue of the Biomedical Journal explores emerging perspectives across neuropsychiatry, metabolism, aging, regenerative medicine, artificial intel...

Artificial Intelligence-Powered Cystoscopy Diagnostic Support System: Clinical Application of Multiarchitecture Deep Learning Models.

PURPOSE: Cystoscopy is a crucial diagnostic tool in urology for the detection and evaluation of bladder lesions. However, its diagnostic accuracy (ACC...

Age-Dependent z Scores and eGFR-Adjusted Reference Ranges for Neurofilament Light: A Practical Approach for Clinical Laboratories.

BACKGROUND: Neurofilament light (NfL) has emerged as a sensitive biomarker of neuroaxonal damage across a wide spectrum of neurological conditions, in...

Automated recognition of surgical phases in robot-assisted radical prostatectomy using a Hiera-based vision transformer.

OBJECTIVES: To construct a reliable robot-assisted radical prostatectomy (RARP) surgical phase-recognition model and sought to deploy the model within...

Interpretable machine learning model for predicting hemorrhage following ultrasound-guided percutaneous renal biopsy: a multicenter study.

OBJECTIVE: This study aims to develop and validate an interpretable machine learning (ML) model for predicting post-procedural hemorrhage (PH) after u...

Machine learning-based prognostic model integrating preoperative HALP score and lactate dehydrogenase for predicting postoperative recurrence of prostate cancer.

OBJECTIVE: Postoperative biochemical recurrence (BCR) of prostate cancer (PCa) remains a major clinical challenge, and traditional risk assessment sys...

Machine-learning algorithms identifies sTREM1 has a key biomarker for outcome prediction in critically ill.

INTRODUCTION: Prognostic assessment in critically ill patients traditionally relies on severity scores or single biomarkers, each with limited ability...

Assessing multiple chatGPT versions on novel content in the Taiwan urology board examination: accuracy, speed, and domain-specific performance.

PURPOSE: Large language models demonstrate increasing utility in healthcare; however, their capacity to handle newly developed medical content beyond ...

Multimodal Prediction of Renal Tumor Malignancy From Radiology Reports and Structured Electronic Health Records: Retrospective Cohort Study.

BACKGROUND: Accurate preoperative prediction of renal tumor malignancy is critical for guiding decisions and reducing overtreatment, as a substantial ...

Usability and Usefulness of Machine Learning-Based Clinical Decision Support Software in Primary Care: Survey of Users in a Prospective Observational Study.

BACKGROUND: The successful implementation of decision support systems promises to enhance high-quality care. However, the successful implementation of...

Machine learning-based prediction of a high-risk kidney function trajectory class after acute kidney injury.

OBJECTIVES: Monitoring kidney function after acute kidney injury (AKI) hospitalisation is essential for identifying patients at risk of rapid progress...