Abstract
Rapid point-of-care (POC) diagnosis of bacterial infection diseases provides clinical benefits of prompt initiation of antimicrobial therapy and reduction of the overuse/misuse of unnecessary antibiotics for nonbacterial infections. We present here a POC compatible method for rapid bacterial infection detection in 10 min. We use a large-volume solution scattering imaging (LVSi) system with low magnifications (1-2×) to visualize bacteria in clinical samples, thus eliminating the need for culture-based isolation and enrichment. We tracked multiple intrinsic phenotypic features of individual cells in a short video. By clustering these features with a simple machine learning algorithm, we can differentiate Escherichia coli from similar-sized polystyrene beads, distinguish bacteria with different shapes, and distinguish E. coli from urine particles. We applied the method to detect urinary tract infections in 104 patient urine samples with a 30 s LVSi video, and the results showed 92.3% accuracy compared with the clinical culture results. This technology provides opportunities for rapid bacterial infection diagnosis at POC settings.
Original language | English (US) |
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Pages (from-to) | 2262-2272 |
Number of pages | 11 |
Journal | ACS sensors |
Volume | 7 |
Issue number | 8 |
DOIs | |
State | Published - Aug 26 2022 |
Externally published | Yes |
Keywords
- UTI screening
- bacteria detection
- machine learning
- multiple phenotypic features
- solution scattering imaging
ASJC Scopus subject areas
- Bioengineering
- Instrumentation
- Process Chemistry and Technology
- Fluid Flow and Transfer Processes