Urology

Machine learning models for predicting severe acute kidney injury in patients with sepsis-induced myocardial injury.

Severe acute kidney injury (sAKI) is a prevalent and serious complication among patients with sepsis-induced myocardial injury (SIMI). Prompt and early prediction of sAKI has an important role in timely intervention, ultimately improving the patients' survival rate. This study aimed to establish machine learning models to predict sAKI via thorough analysis of data derived from electronic medical r...

UNIK (Urologic Non-Neoplastic Investigation of Kidneys): a machine learning approach to decode benign lesion.

PURPOSE: Predicting the likelihood of benign neoplasia in patients with suspected renal cell carcinoma (RCC) is a cornerstone of presurgical planning. We sought to create and validate U.N.I.K., a machine learning (ML) model capable of predicting benign lesions on final histological report.

Prediction of depression risk in middle-aged and elderly Cardiovascular-Kidney-Metabolic syndrome patients by social and environmental determinants of health: an interpretable machine learning approach using longitudinal data from China.

BACKGROUND: Cardiovascular-Kidney-Metabolic (CKM) syndrome is a systemic disease characterized by pathophysiological interactions between the cardiova...

Evaluation of early donor renal function recovery after living-donor nephrectomy as a predictor of allograft failure after kidney transplantation: a longitudinal cohort and machine learning study.

BACKGROUND: Kidney transplant recipients continue to face a significant long-term risk of death-censored allograft failure (DCGF). We aimed to evaluat...

Social bonding between humans, animals, and robots: Dogs outperform AIBOs, their robotic replicas, as social companions.

In the evolving landscape of technology, robots have emerged as social companions, prompting an investigation into social bonding between humans and r...

Artificial intelligence may enhance the role of magnetic resonance imaging in prostate cancer focal therapy.

SPCF-YOLO: An Efficient Feature Optimization Model for Real-Time Lung Nodule Detection.

Accurate pulmonary nodule detection in CT imaging remains challenging due to fragmented feature integration in conventional deep learning models. This...

A machine learning-based prediction model for sepsis-associated delirium in intensive care unit patients with sepsis-associated acute kidney injury.

Sepsis-associated acute kidney injury (SA-AKI) patients in the ICU often suffer from sepsis-associated delirium (SAD), which is linked to unfavorable ...

Unsupervised learning-based quantitative analysis of CT intratumoral subregions predicts risk stratification of bladder cancer patients.

BACKGROUND: Preoperative diagnosis of muscle invasion and American Joint Committee on Cancer (AJCC) stage plays a crucial role in guiding treatment st...

Actor critic with experience replay-based automatic treatment planning for prostate cancer intensity modulated radiotherapy.

BACKGROUND: Achieving highly efficient treatment planning in intensity-modulated radiotherapy (IMRT) is challenging due to the complex interactions be...

Study of AI algorithms on mpMRI and PHI for the diagnosis of clinically significant prostate cancer.

OBJECTIVE: To study the feasibility of multiple factors in improving the diagnostic accuracy of clinically significant prostate cancer (csPCa).

Discriminating Clear Cell From Non-Clear Cell Renal Cell Carcinoma: A Machine Learning Approach Using Contrast-enhanced Ultrasound Radiomics.

OBJECTIVE: The aim of this investigation is to assess the clinical usefulness of a machine learning model using contrast-enhanced ultrasound (CEUS) ra...

One-Drop Serum Screening Test to Monitor Tissue Iron Accumulation.

Although iron is an essential element for vital body functions, iron overload (IO) is accompanied by significant cellular damage due to its accumulati...

AI, docking, and molecular dynamics to track the binding of structural peptides to different keratin models.

The present work shows a computational approach to assess the interactions of different nature-inspired peptides with hair keratin models. An updated ...

The value of artificial intelligence in PSMA PET: a pathway to improved efficiency and results.

INTRODUCTION: This systematic review investigates the potential of artificial intelligence (AI) in improving the accuracy and efficiency of prostate-s...

Using AI to triage patients without clinically significant prostate cancer using biparametric MRI and PSA.

OBJECTIVES: To train and evaluate the performance of a machine learning triaging tool that identifies MRI negative for clinically significant prostate...

Development of an immune scoring system based on exosome-related gene expression for prognosis and treatment response prediction in breast cancer.

Breast cancer (BC) is a heterogeneous disease with diverse subtypes that influence prognosis and treatment outcomes. While advances have been made in ...

Radiomics-based differentiation of upper urinary tract urothelial and renal cell carcinoma in preoperative computed tomography datasets.

BACKGROUND: To investigate a non-invasive radiomics-based machine learning algorithm to differentiate upper urinary tract urothelial carcinoma (UTUC) ...

Strategies for Treatment De-escalation in Metastatic Renal Cell Carcinoma.

Immune checkpoint inhibitors (ICIs) and targeted therapies have revolutionized the management of metastatic renal cell carcinoma (mRCC). Currently, th...

Towards robust medical machine olfaction: Debiasing GC-MS data enhances prostate cancer diagnosis from urine volatiles.

Prostate cancer (PCa) is a major, and increasingly global, health concern with current screening and diagnostic tools' severe limitations causing unne...