Urology

Latest AI and machine learning research in urology for healthcare professionals.

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Prospective Blinded evaluation of Thermalytix, an artificial intelligence-enhanced breast thermal imaging software, correlated with radiologist-interpreted mammograms: Results of an exploratory study in Zambia

While mammography is commonly used for breast cancer detection, its widespread implementation in resource-constrained nations is challenging. Artificial intelligence-based Thermalytix is a low-cost, portable, radiation-free, automated test for breast cancer detection in women of all ages. Although used in India, the efficacy of Thermalytix has not been tested in an African population. To assess th...

Development and Validation of Machine Learning Models for Adverse Events after Cardiac Surgery

Early recognition of adverse events after cardiac surgery is vital for treatment. However, the widely used Society of Thoracic Surgery (STS) risk model has modest performance in predicting adverse events and only applies <80% of cardiac surgeries. To develop and validate machine learning (ML) models for predicting outcomes after cardiac surgery. ML models, referred as Roux-MMC model, were develope...

Exploring multidrug resistance patterns in community-acquired E. coli urinary tract infections with machine learning

While associations of antibiotic resistance traits are not random in multidrug-resistant (MDR) bacteria, clinically relevant resistance patterns remai...

XGBoost-Based Prediction of ICU Mortality in Sepsis-Associated Acute Kidney Injury Patients Using MIMIC-IV Database with Validation from eICU Database

Sepsis-Associated Acute Kidney Injury (SA-AKI) leads to high mortality in intensive care. This study develops machine learning models using the Medica...

Machine Learning Based Classification of Aggressive and Malignant Renal Tumors from Multimodal Data

This study aimed to develop and evaluate a machine learning pipeline using multiphase contrast-enhanced CT images and clinical data to classify renal ...

Unbiased multi-omics network-based data integration allows clinically relevant outcome-predicting clustering of individuals with heart failure

Heart failure is a multifaceted clinical syndrome, in which the heart fails to supply adequate blood to meet the body’s oxygen and nutrients needs. Ev...

Development of an artificial intelligence-generated, explainable treatment recommendation system for urothelial carcinoma and renal cell carcinoma to support multidisciplinary cancer conferences

Decisions on the best available treatment in clinical oncology are based on expert opinions in multidisciplinary cancer conferences (MCC). Artificial ...

Performance of an artificial intelligence foundation model for prostate radiotherapy segmentation

Artificial intelligence (AI) foundation models such as Segment Anything Model 2 (SAM 2) offer potential for semi-automated image segmentation with min...

InfEHR: Resolving Clinical Uncertainty through Deep Geometric Learning on Electronic Health Records

Electronic health records (EHRs) contain multimodal data that can inform diagnostic and prognostic clinical decisions but are often unsuited for advan...

Predicting Prostate Cancer Without a Prostate: A Potential Problem with AI

Machine learning (ML) algorithms have demonstrated great potential for the identification and classification of prostate cancer from Magnetic Resonanc...

nnDoseNet: Intuitive and Flexible Deep Learning Framework to Train and Evaluate Radiotherapy Dose Prediction Models

Radiotherapy (RT) dose optimization is often labor-intensive, requiring repeated manual adjustments to achieve clinically acceptable plans. In this wo...

Transformer-based multiclass segmentation pipeline for basic kidney histology

Multiclass segmentation of microanatomy in kidney biopsies is an important and non-trivial task in computational renal pathology. In a multicenter stu...

Patient Attitudes Toward Artificial Intelligence in Cancer Care: A Scoping Review

To synthesize existing literature on patient attitudes toward AI in cancer care and identify knowledge gaps that can inform future research and clinic...

Utilizing Machine Learning Models to Predict Acute Kidney Injury in Septic Patients from MIMIC-III Database

Sepsis is a severe condition that causes the body to respond incorrectly to an infection. This reaction can subsequently cause organ failure, a major ...

Artificial Intelligence for Chronic Kidney Disease Early Detection and Prognosis

The integration of Artificial Intelligence (AI) in the early detection and prognosis of Chronic Kidney Disease (CKD) is revolutionizing nephrology by ...

Evaluating the Reporting Quality of 21,041 Randomized Controlled Trial Articles

Incomplete reporting of a study’s methods and results hinders efforts to evaluate and reproduce research findings in randomized controlled trials (RCT...

Development of a Risk Prediction Model for Sepsis-Related Delirium Based on Multiple Machine Learning Approaches and an Online Calculator

Sepsis-associated delirium (SAD) occurs due to disruptions in neurotransmission linked to inflammatory responses from infections. It poses significant...

Computational characterization of lymphocyte topology on whole slide images of glomerular diseases

The complexity of distribution of inflammatory cells in the kidney is not well captured by conventional semiquantitative visual assessment. This study...

Prompts to Table: Specification and Iterative Refinement for Clinical Information Extraction with Large Language Models

Extracting structured data from free-text medical records at scale is laborious, and traditional approaches struggle in complex clinical domains. We p...

Circulating extracellular vesicles in serum carry Trop2 marker for prostate cancer liquid biopsy and clinical care

Extracellular vesicles (EVs) are lipid nano-to-micro-sized vesicles increasingly identified as valuable liquid biopsy tools for medical applications. ...

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