AIMC Topic: Urinary Tract Infections
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Antibiotic profile classification of Proteus mirabilis using machine learning: An investigation into multidimensional radiomics features.
Computers in biology and medicine
Sep 10, 2024
Antimicrobial resistance (AMR) presents a significant threat to global healthcare. Proteus mirabilis causes catheter-associated urinary tract infections (CAUTIs) and exhibits increased antibiotic resistance. Traditional diagnostics still rely on cult...
LC-SRM Combined With Machine Learning Enables Fast Identification and Quantification of Bacterial Pathogens in Urinary Tract Infections.
Molecular & cellular proteomics : MCP
Aug 22, 2024
Urinary tract infections (UTIs) are a worldwide health problem. Fast and accurate detection of bacterial infection is essential to provide appropriate antibiotherapy to patients and to avoid the emergence of drug-resistant pathogens. While the gold s...
C-reactive protein (CRP) evaluation in human urine using optical sensor supported by machine learning.
Scientific reports
Aug 14, 2024
The rapid and sensitive indicator of inflammation in the human body is C-Reactive Protein (CRP). Determination of CRP level is important in medical diagnostics because, depending on that factor, it may indicate, e.g., the occurrence of inflammation o...
Unbiased identification of risk factors for invasive Escherichia coli disease using machine learning.
BMC infectious diseases
Aug 8, 2024
BACKGROUND: Invasive Escherichia coli disease (IED), also known as invasive extraintestinal pathogenic E. coli disease, is a leading cause of sepsis and bacteremia in older adults that can result in hospitalization and sometimes death and is frequent...
Artificial intelligence and machine learning applications in urinary tract infections identification and prediction: a systematic review and meta-analysis.
World journal of urology
Aug 1, 2024
BACKGROUND: Urinary tract infections (UTIs) have been one of the most common bacterial infections in clinical practice worldwide. Artificial intelligence (AI) and machine learning (ML) based algorithms have been increasingly applied in UTI case ident...
Identifying signs and symptoms of urinary tract infection from emergency department clinical notes using large language models.
Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
Apr 3, 2024
BACKGROUND: Natural language processing (NLP) tools including recently developed large language models (LLMs) have myriad potential applications in medical care and research, including the efficient labeling and classification of unstructured text su...
Machine Learning-Assistant Colorimetric Sensor Arrays for Intelligent and Rapid Diagnosis of Urinary Tract Infection.
ACS sensors
Mar 26, 2024
Urinary tract infections (UTIs), which can lead to pyelonephritis, urosepsis, and even death, are among the most prevalent infectious diseases worldwide, with a notable increase in treatment costs due to the emergence of drug-resistant pathogens. Cur...
Assisting the infection preventionist: Use of artificial intelligence for health care-associated infection surveillance.
American journal of infection control
Mar 14, 2024
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
Feb 5, 2024
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
Feb 1, 2024
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 ...
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