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
- public health
- high speed
- high throughput
- single cell
- real time pcr
- artificial intelligence
- optical coherence tomography
- biofilm formation
- cell therapy
- sensitive detection
- stem cells
- big data
- rna seq
- mesenchymal stem cells
- mass spectrometry
- current status
- quantum dots
- pseudomonas aeruginosa
- staphylococcus aureus