Urology

Development and validation of a framework for registration of whole-mount radical prostatectomy histopathology with three-dimensional transrectal ultrasound.

PURPOSE: Artificial intelligence (AI) has the potential to improve diagnostic imaging on multiple levels. To develop and validate these AI-assisted modalities a reliable dataset is of utmost importance. The registration of imaging to pathology is an essential step in creating such a dataset. This study presents a comprehensive framework for the registration of 3D transrectal ultrasound (TRUS) to r...

Machine learning models to predict osteoporosis in patients with chronic kidney disease stage 3-5 and end-stage kidney disease.

Chronic kidney disease-mineral bone disorder is a common complication in patients with chronic kidney disease (CKD) and end-stage kidney disease (ESKD), and it increases the risk of osteoporosis and fractures. This study aimed to develop predictive machine-learning (ML) models to identify osteoporosis risk in patients with CKD stages 3-5 and ESKD. We retrospectively analyzed a de-identified osteop...

Urinary biomarkers of kidney transplant rejection.

PURPOSE OF REVIEW: Despite the introduction of many new immunosuppressive medications, allograft rejection remains a significant complication in trans...

An ensemble learning model to predict lymph node metastasis in early gastric cancer.

Lymph node metastasis is a critical factor for determining therapeutic strategies and assessing the prognosis of early gastric cancer. This work aimed...

Artificial intelligence-based personalised rituximab treatment protocol in membranous nephropathy (iRITUX): protocol for a multicentre randomised control trial.

INTRODUCTION: Membranous nephropathy is an autoimmune kidney disease and the most common cause of nephrotic syndrome in non-diabetic Caucasian adults....

Machine Vision Augmentation to Detect Detrusor Overactivity in Overactive Bladder: A Frontier of Artificial Intelligence Application in Functional Urology-Proof of Concept Clinical Study.

INTRODUCTION: Overactive bladder (OAB) is a common urological condition with increasing prevalence, especially in an aging population. Diagnosing and ...

Will artificial intelligence (AI) replace cytopathologists: a scoping review of current applications and evidence of A.I. in urine cytology.

PURPOSE: Urine cytology, while valuable in facilitating the detection and surveillance of bladder cancer, has notable limitations. The application of ...

Detection of kidney bean leaf spot disease based on a hybrid deep learning model.

Rapid diagnosis of kidney bean leaf spot disease is crucial for ensuring crop health and increasing yield. However, traditional machine learning metho...

Clinical implications of deep learning based image analysis of whole radical prostatectomy specimens.

Prostate cancer (PCa) diagnosis faces significant challenges due to its complex pathological characteristics and insufficient pathologist resources. W...

[Artificial intelligence-based fusion prostate biopsy].

Proton dose calculation with transformer: Transforming spot map to dose.

BACKGROUND: Conventional proton dose calculation methods are either time- and resource-intensive, like Monte Carlo (MC) simulations, or they sacrifice...

Exploring the Incremental Value of Aorta Enhancement Normalization Method in Evaluating Renal Cell Carcinoma Histological Subtypes: A Multi-center Large Cohort Study.

RATIONALE AND OBJECTIVES: The classification of renal cell carcinoma (RCC) histological subtypes plays a crucial role in clinical diagnosis. However, ...

Artificial intelligence-based multimodal prediction for nuclear grading status and prognosis of clear cell renal cell carcinoma: a multicenter cohort study.

BACKGROUND: The assessment of the International Society of Urological Pathology (ISUP) nuclear grade is crucial for the management and treatment of cl...

Machine learning for risk prediction of acute kidney injury in patients with diabetes mellitus combined with heart failure during hospitalization.

This study aimed to develop a machine learning (ML) model for predicting the risk of acute kidney injury (AKI) in diabetic patients with heart failure...

Fine-tuned deep learning models for early detection and classification of kidney conditions in CT imaging.

The kidney plays a vital role in maintaining homeostasis, but lifestyle factors and diseases can lead to kidney failures. Early detection of kidney di...

Deep Learning-Based Auto-Segmentation for Liver Yttrium-90 Selective Internal Radiation Therapy.

The aim was to evaluate a deep learning-based auto-segmentation method for liver delineation in Y-90 selective internal radiation therapy (SIRT). A de...

ALADDIN: A Machine Learning Approach to Enhance the Prediction of Significant Fibrosis or Higher in Metabolic Dysfunction-Associated Steatotic Liver Disease.

INTRODUCTION: The recent US Food and Drug Administration approval of resmetirom for treating metabolic dysfunction-associated steatohepatitis in patie...

Imaging and spatially resolved mass spectrometry applications in nephrology.

The application of spatially resolved mass spectrometry (MS) and MS imaging approaches for studying biomolecular processes in the kidney is rapidly gr...

Deep learning techniques for proton dose prediction across multiple anatomical sites and variable beam configurations.

To evaluate the impact of beam mask implementation and data aggregation on artificial intelligence-based dose prediction accuracy in proton therapy, w...

Exploring new drug treatment targets for immune related bone diseases using a multi omics joint analysis strategy.

In the field of treatment and prevention of immune-related bone diseases, significant challenges persist, necessitating the urgent exploration of new ...