BACKGROUND: The Paris System for Urine Cytopathology (the Paris System) has succeeded in making the analysis of liquid-based urine preparations more reproducible. Any algorithm seeking to automate this system must accurately estimate the nuclear-to-c...
BMC medical informatics and decision making
31443706
BACKGROUND: A substantial proportion of microbiological screening in diagnostic laboratories is due to suspected urinary tract infections (UTIs), yet approximately two thirds of urine samples typically yield negative culture results. By reducing the ...
Trichomonas vaginalis (T. vaginalis) detection remains an unsolved problem in using of automated instruments for urinalysis. The study proposes a machine learning (ML)-based strategy to increase the detection rate of T. vaginalis in urine. On the bas...
Urine sediment recognition is attracting growing interest in the field of computer vision. A multi-view urine cell recognition method based on multi-view deep residual learning is proposed to solve some existing problems, such as multi-view cell gray...
PURPOSE: To establish the correlation model between Traditional Chinese Medicine (TCM) constitution and physical examination indexes by backpropagation neural network (BPNN) technology. A new method for the identification of TCM constitution in clini...
OBJECTIVE: Current urinary tract infection (UTI) diagnostic strategies that rely on leukocyte esterase have limited accuracy. We performed an aptamer-based proteomics pilot study to identify urine protein levels that could differentiate a culture pro...
Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases
33038526
OBJECTIVES: Urine is the most common material tested in clinical microbiology laboratories. Automated analysis is already performed, permitting quicker results and decreasing the laboratory technologist's (LT) workload. These automatic systems have i...
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...
Clinical chemistry and laboratory medicine
35778953
OBJECTIVES: Automated machine learning (AutoML) tools can help clinical laboratory professionals to develop machine learning models. The objective of this study was to develop a novel formula for the estimation of urine osmolality using an AutoML too...
The growing transition to digital microbiology in clinical laboratories creates the opportunity to interpret images using software. Software analysis tools can be designed to use human-curated knowledge and expert rules, but more novel artificial int...