Evaluation of BacterioScan 216Dx in Comparison to Urinalysis as a Screening Tool for Diagnosis of Urinary Tract Infections in Children

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

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 hypothesized that bacteria with variable bacterial densities and different Gram staining reactions would generate different speckle images. After deep learning of speckle patterns generated by various densities of bacteria in artificial urine, we validated the model in an independent set of clinical urine samples in a tertiary hospital. Even at a low bacterial density cutoff (1,000 CFU/mL), the model achieved a predictive accuracy of 90.9% for positive urine culture. At a cutoff of 50,000 CFU/mL, it showed a better accuracy of 98.5%. The model achieved satisfactory accuracy at both cutoff levels for predicting the Gram staining reaction. Considering only 30 min of analysis, our method appears as a new screening tool for predicting the presence of bacteria before urine culture. IMPORTANCE This study performed deep learning of multiple laser scattering patterns by the bacteria in urine to predict positive urine culture. Conventional urine analyzers have limited performance in identifying bacteria in urine. This novel method showed a satisfactory accuracy taking only 30 min of analysis without conventional urine culture. It was also developed to predict the Gram staining reaction of the bacteria. It can be used as a standalone screening tool for urinary tract infection.

Rapid Antimicrobial Susceptibility Testing on Clinical Urine Samples by Video-Based Object Scattering Intensity Detection

To combat the ongoing public health threat of antibiotic-resistant infections, a technology that can quickly identify infecting bacterial pathogens and concurrently perform antimicrobial susceptibility testing (AST) in point-of-care settings is needed. Here, we develop a technology for point-of-care AST with a low-magnification solution scattering imaging system and a real-time video-based object scattering intensity detection method. The low magnification (1-2×) optics provides sufficient volume for direct imaging of bacteria in urine samples, avoiding the time-consuming process of culture-based bacterial isolation and enrichment. Scattering intensity from moving bacteria and particles in the sample is obtained by subtracting both spatial and temporal background from a short video. The time profile of scattering intensity is correlated with the bacterial growth rate and bacterial response to antibiotic exposure. Compared to the image-based bacterial tracking and counting method we previously developed, this simple image processing algorithm accommodates a wider range of bacterial concentrations, simplifies sample preparation, and greatly reduces the computational cost of signal processing. Furthermore, development of this simplified processing algorithm eases implementation of multiplexed detection and allows real-time signal readout, which are essential for point-of-care AST applications. To establish the method, 130 clinical urine samples were tested, and the results demonstrated an accuracy of ∼92% within 60-90 min for UTI diagnosis. Rapid AST of 55 positive clinical samples revealed 98% categorical agreement with both the clinical culture results and the on-site parallel AST validation results. This technology provides opportunities for prompt infection diagnosis and accurate antibiotic prescriptions in point-of-care settings.

Accuracy of matrix-assisted laser desorption ionization time-of-flight mass spectrometry for direct bacterial identification from culture-positive urine samples

Background

Urinary tract infection (UTI) is one of the most frequent reasons for antimicrobial therapy. In typical clinical setting, 18–48 h is needed to identify pathogens by urine culture. A rapid method for pathogenic UTI diagnosis by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been developed in recent years.

Methods

This meta-analysis systematically evaluated the accuracy of MALDI-TOF MS for direct identification of bacteria from culture-positive urine samples. We queried the electronic database of Medline and Web of Science to obtain relevant articles.

Results

Nineteen articles involving 4,579 isolates were included after final selection in the meta-analysis. The random-effects pooled identification accuracy of MALDI-TOF MS was 0.82 with 95% confidence interval of 0.79 to 0.86 at the species level. For Gram-negative isolates, the correct identification performance of the species ranged from 0.54 to 0.98, with a cumulative rate of 0.87 (95% CI: 0.83 to 0.91). For Gram-positive isolates, the correct identification rate ranged from 0.32 to 0.80, with a cumulative rate of 0.59 (95% CI: 0.49 to 0.68).

Conclusions

MALDI-TOF MS provides a reliable direct identification of bacteria, particularly in cases of Gram-negative isolates, from clinical urine specimens. Nevertheless, the identification accuracy of this method is moderate for Gram-positive bacteria.