AIMC Topic: Urinary Tract Infections

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Assisting the infection preventionist: Use of artificial intelligence for health care-associated infection surveillance.

American journal of infection control
BACKGROUND: Health care-associated infection (HAI) surveillance is vital for safety in health care settings. It helps identify infection risk factors, enhancing patient safety and quality improvement. However, HAI surveillance is complex, demanding s...

Deep Learning-Based Culture-Free Bacteria Detection in Urine Using Large-Volume Microscopy.

Biosensors
Bacterial infections, increasingly resistant to common antibiotics, pose a global health challenge. Traditional diagnostics often depend on slow cell culturing, leading to empirical treatments that accelerate antibiotic resistance. We present a novel...

A clinical microscopy dataset to develop a deep learning diagnostic test for urinary tract infection.

Scientific data
Urinary tract infection (UTI) is a common disorder. Its diagnosis can be made by microscopic examination of voided urine for markers of infection. This manual technique is technically difficult, time-consuming and prone to inter-observer errors. The ...

Development and validation of artificial intelligence models to predict urinary tract infections and secondary bloodstream infections in adult patients.

Journal of infection and public health
BACKGROUND: Traditional culture methods are time-consuming, making it difficult to utilize the results in the early stage of urinary tract infection (UTI) management, and automated urinalyses alone show insufficient performance for diagnosing UTIs. S...

Magnetic Soft Robot for Minimally Invasive Urethral Catheter Biofilm Eradication.

ACS nano
Catheter-related biofilm infection remains the main problem for millions of people annually, affecting morbidity, mortality, and quality of life. Despite the recent advances in the prevention of biofilm formation, alternative methods for biofilm prev...

Clinically explainable machine learning models for early identification of patients at risk of hospital-acquired urinary tract infection.

The Journal of hospital infection
BACKGROUND: Machine learning (ML) models for early identification of patients at risk of hospital-acquired urinary tract infection (HA-UTI) may enable timely and targeted preventive and therapeutic strategies. However, clinicians are often challenged...

Rapid Bacterial Detection in Urine Using Laser Scattering and Deep Learning Analysis.

Microbiology spectrum
Images of laser scattering patterns generated by bacteria in urine are promising resources for deep learning. However, floating bacteria in urine produce dynamic scattering patterns and require deep learning of spatial and temporal features. We hypot...

Upper or lower tract approach for duplex anomalies? A bi-institutional comparative analysis of robot-assisted approaches.

Journal of robotic surgery
Optimal management of duplication anomalies may include an upper or lower tract surgical approach. In the contemporary era, the robot-assisted laparoscopic heminephrectomy (RALHN) and robot-assisted laparoscopic ipsilateral ureteroureterostomy (RALIU...

Prediction of post-stroke urinary tract infection risk in immobile patients using machine learning: an observational cohort study.

The Journal of hospital infection
BACKGROUND: Urinary tract infection (UTI) is one of major nosocomial infections significantly affecting the outcomes of immobile stroke patients. Previous studies have identified several risk factors, but it is still challenging to accurately estimat...

Rapid identification of the resistance of urinary tract pathogenic bacteria using deep learning-based spectroscopic analysis.

Analytical and bioanalytical chemistry
The resistance of urinary tract pathogenic bacteria to various antibiotics is increasing, which requires the rapid detection of infectious pathogens for accurate and timely antibiotic treatment. Here, we propose a rapid diagnosis strategy for the ant...