Rapid Detection of Urinary Tract Infection in 10 min by Tracking Multiple Phenotypic Features in a 30 s Large-Volume Scattering Video of Urine Microscopy.
Fenni ZhangManni MoJiapei JiangXinyu ZhouMichelle McBrideYunze YangKenta S ReillyThomas E GrysShelley E HaydelNongjian TaoShaopeng WangPublished in: ACS sensors (2022)
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.
Keyphrases
- urinary tract infection
- escherichia coli
- machine learning
- loop mediated isothermal amplification
- high resolution
- induced apoptosis
- chronic pain
- artificial intelligence
- high throughput
- cell proliferation
- cell death
- signaling pathway
- mesenchymal stem cells
- mass spectrometry
- label free
- oxidative stress
- rna seq
- photodynamic therapy
- cystic fibrosis
- bone marrow
- multidrug resistant
- pseudomonas aeruginosa
- big data
- sensitive detection
- real time pcr
- solid state