Rapid
and low-cost pathogen diagnostic approaches are critical
for clinical decision-making procedures. Cultivating bacteria often
takes days to identify pathogens and provide antimicrobial susceptibilities.
The delay in diagnosis may result in compromised treatment and inappropriate
antibiotic use. Over the past decades, molecular-based techniques
have significantly shortened pathogen identification turnaround time
with high accuracy. However, these assays often use complex fluorescent
labeling and nucleic acid amplification processes, which limit their
use in resource-limited settings. In this work, we demonstrate a wash-free
molecular agglutination assay with a straightforward mixing and incubation
step that significantly simplifies procedures of molecular testing.
By targeting the 16S rRNA gene of pathogens, we perform a rapid pathogen
identification within 30 min on a dark-field imaging microfluidic
cytometry platform. The dark-field images with low background noise
can be obtained using a narrow beam scanning technique with off-the-shelf
complementary metal oxide semiconductor (CMOS) imagers such as smartphone
cameras. We utilize a machine learning algorithm to deconvolute topological
features of agglutinated clusters and thus quantify the abundance
of bacteria. Consequently, we unambiguously distinguish Escherichia
coli positive from other E. coli negative
among 50 clinical urinary tract infection samples with 96% sensitivity
and 100% specificity. Furthermore, we also apply this quantitative
detection approach to achieve rapid antimicrobial susceptibility testing
within 3 h. This work exhibits easy-to-use protocols, high sensitivity,
and short turnaround time for point-of-care testing uses.