Deep Learning-Based Culture-Free Bacteria Detection in Urine Using Large-Volume Microscopy

Rafael Iriya, Brandyn Braswell, Manni Mo, Fenni Zhang, Shelley E. Haydel, Shaopeng Wang

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number89
JournalBiosensors
Volume14
Issue number2
DOIs
StatePublished - Feb 2024

Keywords

  • bacteria detection
  • CNN
  • deep learning
  • light scattering microscopy
  • LVM
  • UTI diagnostics

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biotechnology
  • Biomedical Engineering
  • Instrumentation
  • Engineering (miscellaneous)
  • Clinical Biochemistry

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