Urology

Exploring the subtle and novel renal pathological changes in diabetic nephropathy using clustering analysis with deep learning.

To decrease the number of chronic kidney disease (CKD), early diagnosis of diabetic kidney disease is required. We performed invariant information clustering (IIC)-based clustering on glomerular images obtained from nephrectomized kidneys of patients with and without diabetes. We also used visualizing techniques (gradient-weighted class activation mapping (Grad-CAM) and generative adversarial netw...

Difference between estimated glomerular filtration rate based on cystatin C versus creatinine and cardiovascular-kidney-metabolic health.

BACKGROUND: The difference between the estimated glomerular filtration rate (eGFR) calculated from cystatin C and creatinine (eGFRdiff) serves as a biomarker of kidney function impairment. However, the role of eGFRdiff in cardiovascular-kidney-metabolic (CKM) health and its impact on mortality in CKM syndrome patients has not yet been studied.

A novel deep learning-based pipeline architecture for pulp stone detection on panoramic radiographs.

OBJECTIVES: Pulp stones are ectopic calcifications located in pulp tissue. The aim of this study is to introduce a novel method for detecting pulp sto...

Deep Learning for Predicting Difficulty in Radical Prostatectomy: A Novel Evaluation Scheme.

OBJECTIVE: To explore new metrics for assessing radical prostatectomy difficulty through a two-stage deep learning method from preoperative magnetic r...

Deep Learning and Numerical Analysis for Bladder Outflow Obstruction and Detrusor Underactivity Diagnosis in Men: A Novel Urodynamic Evaluation Scheme.

OBJECTIVES: To automatically identify and diagnose bladder outflow obstruction (BOO) and detrusor underactivity (DUA) in male patients with lower urin...

An Integrated Microfluidic Microwave Array Sensor with Machine Learning for Enrichment and Detection of Mixed Biological Solution.

In this work, an integrated microfluidic microwave array sensor is proposed for the enrichment and detection of mixed biological solution. In individu...

Integrated multi-omics analysis identifies a machine learning-derived signature for predicting prognosis and therapeutic vulnerability in clear cell renal cell carcinoma.

AIMS: Clear cell renal cell carcinoma (ccRCC) shows considerable variation within and between tumors, presents varying treatment responses among patie...

Clinical Application of Deep Learning-Assisted Needles Reconstruction in Prostate Ultrasound Brachytherapy.

PURPOSE: High dose rate (HDR) prostate brachytherapy (BT) procedure requires image-guided needle insertion. Given that general anesthesia is often emp...

Glo-net: A dual task branch based neural network for multi-class glomeruli segmentation.

Accurate segmentation and classification of glomeruli are fundamental to histopathology slide analysis in renal pathology, which helps to characterize...

Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types.

Recently, as the number of cancer patients has increased, much research is being conducted for efficient treatment, including the use of artificial in...

Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma.

The connection between metabolic reprogramming and tumor progression has been demonstrated in an increasing number of researches. However, further res...

Application of ChatGPT-assisted problem-based learning teaching method in clinical medical education.

INTRODUCTION: Artificial intelligence technology has a wide range of application prospects in the field of medical education. The aim of the study was...

Prediction of p-phenylenediamine antioxidant concentrations in human urine using machine learning models.

p-phenylenediamine antioxidants (PPDs) are extensively used in rubber manufacturing for their potent antioxidative properties, but PPDs and 2-anilino-...

BMA-Net: A 3D bidirectional multi-scale feature aggregation network for prostate region segmentation.

BACKGROUND AND OBJECTIVE: Accurate segmentation of the prostate region in magnetic resonance imaging (MRI) is crucial for prostate-related diagnoses. ...

Patient- and clinician-based evaluation of large language models for patient education in prostate cancer radiotherapy.

BACKGROUND: This study aims to evaluate the capabilities and limitations of large language models (LLMs) for providing patient education for men under...

Deep learning-based lymph node metastasis status predicts prognosis from muscle-invasive bladder cancer histopathology.

PURPOSE: To develop a deep learning (DL) model based on primary tumor tissue to predict the lymph node metastasis (LNM) status of muscle invasive blad...

Prediction of mortality risk in patients with severe community-acquired pneumonia in the intensive care unit using machine learning.

The aim of this study was to develop and validate a machine learning-based mortality risk prediction model for patients with severe community-acquired...

Amphotericin B tissue penetration and pharmacokinetics in healthy and -infected rats: insights from microdialysis and population modeling.

INTRODUCTION: This study evaluated the relationship between total plasma and free kidney concentrations of amphotericin B (AmB) in healthy and -infect...

A stacking ensemble system for identifying the presence of histological variants in bladder carcinoma: a multicenter study.

PURPOSE: To create a system to enable the identification of histological variants of bladder cancer in a simple, efficient, and noninvasive manner.

Harnessing machine learning to predict prostate cancer survival: a review.

The prediction of survival outcomes is a key factor in making decisions for prostate cancer (PCa) treatment. Advances in computer-based technologies h...