Purpose: Catheter associated urinary tract infection (CAUTI) is the most common healthcare associated infection. A significant knowledge gap exists regarding the necessity of catheter replacement as part of CAUTI treatment. Current guidelines recommend replacement for faster recovery and to prevent recurrences, but adherence is low.
View Article and Find Full Text PDFSignificance: The accurate correlation between optical measurements and pathology relies on precise image registration, often hindered by deformations in histology images. We investigate an automated multi-modal image registration method using deep learning to align breast specimen images with corresponding histology images.
Aim: We aim to explore the effectiveness of an automated image registration technique based on deep learning principles for aligning breast specimen images with histology images acquired through different modalities, addressing challenges posed by intensity variations and structural differences.
Background: The diagnostic process is a key element of medicine but it is complex and prone to errors. Infectious diseases are one of the three categories of diseases in which diagnostic errors can be most harmful to patients. In this study we aimed to estimate the effect of initial misdiagnosis of the source of infection in patients with bacteraemia on 14 day mortality using propensity score methods to adjust for confounding.
View Article and Find Full Text PDFThe absence of a consensus-based reference standard for urinary tract infection (UTI) research adversely affects the internal and external validity of diagnostic and therapeutic studies. This omission hinders the accumulation of evidence for a disease that imposes a substantial burden on patients and society, particularly in an era of increasing antimicrobial resistance. We did a three-round Delphi study involving an international, multidisciplinary panel of UTI experts (n=46) and achieved a high degree of consensus (94%) on the final reference standard.
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