Urology

Diagnostic Performance of Deep Learning Using Retinal Images for Detection of Chronic Kidney Disease: A Systematic Review and Meta-analysis.

BACKGROUND: Multiple investigations have been conducted to diagnose chronic kidney disease (CKD) from photographs of retina using deep learning models, with a wide range of diagnostic performance, which necessitates the importance of a comprehensive review. Therefore, in this meta-analysis, we estimated the detection performance of deep learning models for the diagnosis of CKD using retinal photog...

Ambient Artificial Intelligence Scribes in Urology: Early Lessons From a Single-System Implementation.

Enhancing Robustness of Deep Learning to Batch Effects from Multi-site Data for Segmentation of Clinically Significant Prostate Cancer on MRI.

Deep learning (DL) has shown promise in segmenting clinically significant prostate cancer (csPCa) on MRI. However, batch effects arising from multi-si...

Explainable artificial intelligence reveals key surgical parameters in robot-assisted and open radical prostatectomy.

Preoperative risk stratification for radical prostatectomy is crucial, yet predicting the wide range of postoperative outcomes remains a significant c...

Development and internal validation of an interpretable machine learning model for predicting vancomycin-induced nephrotoxicity in hospitalized children.

INTRODUCTION: Vancomycin is widely used for severe Gram‑positive infections in children, but vancomycin‑induced nephrotoxicity (VIN) limits its safe a...

Diagnosis of Clinically Significant Prostate Cancer in Multiparametric MRI with Pseudo-Localization of Suspected Lesion.

To develop a two-stage diagnostic framework using pseudo-localization for patient-level diagnosis of clinically significant prostate cancer (csPCa) on...

Survival Prediction for Clear Cell Renal Cell Carcinoma Based on Deep Multimodal Synergistic Survival Network.

OBJECTIVE: To propose a deep multimodal synergistic survival analysis framework (Deep Multimodal Synergistic Survival Network, DMSSN) to achieve accur...

Performance-Based Frailty Tools Capture Distinct Risk Phenotypes in Kidney Transplant Candidates: A Comparative Analysis of FRAIL and SPPB.

Frailty predicts adverse outcomes in kidney transplant candidates, but it remains unclear whether different frailty tools identify the same or distinc...

Training-free motion correction for cardiac T1 mapping using DINOv3 features.

Cardiac T1 mapping is susceptible to respiratory motion, particularly due to the substantial contrast variations and signal inversions across differen...

Development and validation of a multidimensional machine learning model for predicting delayed graft function in deceased donor kidney transplantation.

PURPOSE: Delayed graft function (DGF) remains a significant complication following deceased donor kidney transplantation. This study aimed to develop ...

Multimodal artificial intelligence in prostate cancer: integrating multiparametric MRI with clinicopathological, molecular, and functional imaging data.

Multimodal artificial intelligence (AI) is reshaping prostate cancer imaging by moving beyond MRI-only algorithms toward models that integrate multipa...

PRECISE v2 in active surveillance prostate MRI: toward a structured lexicon for longitudinal interpretation.

Active surveillance increasingly incorporates prostate MRI for longitudinal assessment of lesion stability or progression, yet terminology for serial ...

Radiologic, Pathologic, and Deep Learning Predictors of Response to Immune Checkpoint Blockade in Renal Cell Carcinoma Patients Undergoing Post-Treatment Nephrectomy.

Response assessment of primary kidney tumors in the consolidation cytoreductive and neoadjuvant settings offers a unique opportunity to inform postope...

Merging artificial intelligence and retinal vascular geometric parameters: A novel tool for diagnosis and prognosis prediction of diabetic nephropathy.

BACKGROUND: Diabetic nephropathy (DN) is the leading cause of end-stage renal disease. The retinal microvasculature, as the only directly observable m...

NADPH oxidases in immunometabolism and disease pathology: mechanistic networks, pollutant triggers, and therapeutic frontiers.

NADPH oxidases (NOXs) have emerged as central hubs that link environmental, metabolic, and immune cues through spatially organized redox signaling. Ho...

Machine Learning Analysis of Single-Cell Transcriptomics Identifies a B-Cell-Associated Fibroblast Signature Predictive of Renal Fibrosis Severity.

BACKGROUND: Renal fibrosis represents the final common pathway of chronic kidney disease (CKD); however, both its definitive diagnostic biomarkers and...

Predicting Indwelling Catheter Duration in Spinal Cord Injury Patients With Neurogenic Bladder Using Machine Learning.

OBJECTIVE: To develop and validate interpretable machine learning (ML) survival models for predicting indwelling catheter time (ICT) in early-stage sp...

Targeting GLS and LPIN2 in renal fibroblasts: potential therapeutic targets for kidney stone disease identified by integrated multi-omics analysis.

Kidney stones (KS) are a common urological condition, the aetiology of which remains incompletely understood. This study aimed to investigate the key ...

Neoantigen in cancer immunotherapy: mechanism, therapeutic strategies and future perspectives.

Neoantigens are tumor-specific antigens resulting from genetic, transcriptomic, and proteomic changes, making them a promising avenue for personalized...

Corrigendum to "Relationship between the diagnostic probability of chronic kidney disease calculated using artificial intelligence-enhanced electrocardiography and the incidence of cardiovascular events" [International Journal of Cardiology 458 (2026) 134576].