Urology

A machine learning model for predicting worsening renal function using one-year time series data in patients with type 2 diabetes.

BACKGROUND AND AIMS: To prevent end-stage renal disease caused by diabetic kidney disease, we created a predictive model for high-risk patients using machine learning.

Unveiling the utility of artificial intelligence for prediction, diagnosis, and progression of diabetic kidney disease: an evidence-based systematic review and meta-analysis.

OBJECTIVE: The purpose of this study was to conduct a systematic investigation of the potential of artificial intelligence (AI) models in the prediction, detection of diagnostic biomarkers, and progression of diabetic kidney disease (DKD). In addition, we compared the performance of non-logistic regression (LR) machine learning (ML) models to conventional LR prediction models.

Comparison of MRI artificial intelligence-guided cognitive fusion-targeted biopsy versus routine cognitive fusion-targeted prostate biopsy in prostate cancer diagnosis: a randomized controlled trial.

BACKGROUND: Cognitive fusion MRI-guided targeted biopsy (cTB) has been widely used in the diagnosis of prostate cancer (PCa). However, cTB relies heav...

How Useful are Current Chatbots Regarding Urology Patient Information? Comparison of the Ten Most Popular Chatbots' Responses About Female Urinary Incontinence.

This research evaluates the readability and quality of patient information material about female urinary incontinence (fUI) in ten popular artificial ...

A Novel Machine Learning-based Predictive Model of Clinically Significant Prostate Cancer and Online Risk Calculator.

OBJECTIVE: To create a machine-learning predictive model combining prostate imaging-reporting and data system (PI-RADS) score, PSA density, and clinic...

Factors and machine learning models for predicting successful discontinuation of continuous renal replacement therapy in critically ill patients with acute kidney injury: a retrospective cohort study based on MIMIC-IV database.

BACKGROUND: For critically ill patients with acute kidney injury (AKI), there remains controversy regarding the predictive factors affecting the disco...

Investigating Ligand-Mediated Conformational Dynamics of Pre-miR21: A Machine-Learning-Aided Enhanced Sampling Study.

MicroRNAs (miRs) are short, noncoding RNA strands that regulate the activity of mRNAs by affecting the repression of protein translation, and their dy...

Evaluating the effectiveness of AI-powered UrologiQ's in accurately measuring kidney stone volume in urolithiasis patients.

Kidney stones and urolithiasis are kidney diseases that have a significant impact on health and well-being, and their incidence is increasing annually...

Impact of different nephrectomy types on M0 renal cell carcinoma outcomes in a propensity score matching and deep learning study.

There are few analyses comparing complete nephrectomy with resection of the renal parenchyma only (CN) or radical nephrectomy that includes simultaneo...

Modification and Validation of the System Causability Scale Using AI-Based Therapeutic Recommendations for Urological Cancer Patients: A Basis for the Development of a Prospective Comparative Study.

The integration of artificial intelligence, particularly Large Language Models (LLMs), has the potential to significantly enhance therapeutic decision...

IoT based healthcare system using fractional dung beetle optimization enabled deep learning for breast cancer classification.

Breast cancer classification plays a crucial role in healthcare, especially in the diagnosis and monitoring of patients. Traditional methods for class...

Development and validation of a prediction model for ED using machine learning: according to NHANES 2001-2004.

Erectile Dysfunction (ED) is a form of sexual dysfunction in males that imposes significant health and financial burdens globally. Despite its high pr...

A machine learning-based analysis for the definition of an optimal renal biopsy for kidney cancer.

OBJECTIVE: Renal Tumor biopsy (RTB) can assist clinicians in determining the most suitable approach for treatment of renal cancer. However, RTB's limi...

Artificial intelligence algorithms enhance urine cytology reporting confidence in postoperative follow-up for upper urinary tract urothelial carcinoma.

PURPOSE: In Taiwan, the incidence of urothelial carcinoma of the upper urinary tract (UTUC) is high and intravesical recurrence is approximately 22%-4...

Evaluation of ChatGPT as a Reliable Source of Medical Information on Prostate Cancer for Patients: Global Comparative Survey of Medical Oncologists and Urologists.

INTRODUCTION: No consensus exists on performance standards for evaluation of generative artificial intelligence (AI) to generate medical responses. Th...

Advanced Low-Cost Technology for Assessing Metal Accumulation in the Body of a Metropolitan Resident Based on a Neural Network Model.

This study is devoted to creating a neural network technology for assessing metal accumulation in the body of a metropolis resident with short-term an...

The shared biomarkers and immune landscape in psoriatic arthritis and rheumatoid arthritis: Findings based on bioinformatics, machine learning and single-cell analysis.

OBJECTIVE: Psoriatic arthritis (PsA) and rheumatoid arthritis (RA) are the most common types of inflammatory musculoskeletal disorders that share over...

Validation of an artificial intelligence-based prognostic biomarker in patients with oligometastatic Castration-Sensitive prostate cancer.

BACKGROUND: There is a need for clinically actionable prognostic and predictive tools to guide the management of oligometastatic castration-sensitive ...

Automated real-world data integration improves cancer outcome prediction.

The digitization of health records and growing availability of tumour DNA sequencing provide an opportunity to study the determinants of cancer outcom...

Prostate cancer prognosis using machine learning: A critical review of survival analysis methods.

Prostate Cancer is a disease that affects the male reproductive system. The irregularity of the symptoms makes it hard for the clinicians to pinpoint ...