Deep Learning-Based Culture-Free Bacteria Detection in Urine Using Large-Volume Microscopy.
Rafael IriyaBrandyn BraswellManni MoFenni ZhangShelley E HaydelShaopeng WangPublished in: Biosensors (2024)
Bacterial infections, increasingly resistant to common antibiotics, pose a global health challenge. Traditional diagnostics often depend on slow cell culturing, leading to empirical treatments that accelerate antibiotic resistance. We present a novel large-volume microscopy (LVM) system for rapid, point-of-care bacterial detection. This system, using low magnification (1-2×), visualizes sufficient sample volumes, eliminating the need for culture-based enrichment. Employing deep neural networks, our model demonstrates superior accuracy in detecting uropathogenic Escherichia coli compared to traditional machine learning methods. Future endeavors will focus on enriching our datasets with mixed samples and a broader spectrum of uropathogens, aiming to extend the applicability of our model to clinical samples.
Keyphrases
- escherichia coli
- label free
- global health
- loop mediated isothermal amplification
- machine learning
- neural network
- deep learning
- single molecule
- high resolution
- high speed
- high throughput
- public health
- single cell
- real time pcr
- optical coherence tomography
- artificial intelligence
- biofilm formation
- rna seq
- convolutional neural network
- stem cells
- klebsiella pneumoniae
- urinary tract infection
- multidrug resistant
- bone marrow
- pseudomonas aeruginosa