ArticleLight, science & applications2020
Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning.
Article in Light, science & applications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Non-destructive label-free automated identification of bacterial colonies at the species level directly on agar media using digital holography and convolutional neural network algorithms.Microbiology spectrum · 2026Article
- Artificial Intelligence in Foodborne Pathogen Detection from Sensing to Food Safety Systems: A Systematic Review.Foods (Basel, Switzerland) · 2026Review
- Conditional superiorities and unaddressed bottlenecks: a critical review of artificial intelligence for waterborne microbial detection.Applied microbiology and biotechnology · 2026Review
- Meat-Borne Bacterial Pathogen Detection: Conventional, Molecular and Emerging AI-Based Strategies.Diagnostics (Basel, Switzerland) · 2026Review
- Self-Powered Sensors for Environmental Monitoring.Nanomaterials (Basel, Switzerland) · 2026Review
- Progress of Rapid Detection Technology for Aquatic Microorganisms: A Comprehensive Review.Microorganisms · 2026Review
- Novel convolutional neural network for bacterial identification of confocal microscopic datasets.Scientific reports · 2026Article
- Plan meta-objective for sub-micron quantitative phase imaging.Light, science & applications · 2026Article
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- A weakly supervised framework for automated biological assay assessment.Virus research · 2026Article
- OpenLM: an open-source pixel super-resolution platform for lens-free microscopy with applications in bacterial growth monitoring and deep learning-based bacterial detection.Lab on a chip · 2025Article
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- Digital holographic microscopy for rapid bacteria segmentation and counting in microfluidic cartridges: basic considerations and limitations for diagnostic application.Journal of biomedical optics · 2025Article
- Colony-YOLO: A Lightweight Micro-Colony Detection Network Based on Improved YOLOv8n.Microorganisms · 2025Article
- Machine learning in point-of-care testing: innovations, challenges, and opportunities.Nature communications · 2025Review
- Bedside inoculation accelerates detection and improves microbiological diagnosis efficiency compared to delayed inoculation.American journal of translational research · 2025Article
- Spectroscopic characterization of bacterial colonies through UV hyperspectral imaging techniques.Frontiers in chemistry · 2025Article
- From pandemics to preparedness: harnessing AI, CRISPR, and synthetic biology to counter biosecurity threats.Frontiers in public health · 2025Review
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- Apt-Conjugated PDMS-ZnO/Ag-Based Multifunctional Integrated Superhydrophobic Biosensor with High SERS Activity and Photocatalytic Sterilization Performance.International journal of molecular sciences · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early identification of pathogenic bacteria in food, water, and bodily fluids is very important and yet challenging, owing to sample complexities and large sample volumes that need to be rapidly screened. Existing screening methods based on plate counting or molecular analysis present various tradeoffs with regard to the detection time, accuracy/sensitivity, cost, and sample preparation complexity. Here, we present a computational live bacteria detection system that periodically captures coherent microscopy images of bacterial growth inside a 60-mm-diameter agar plate and analyses these time-lapsed holograms using deep neural networks for the rapid detection of bacterial growth and the classification of the corresponding species. The performance of our system was demonstrated by the rapid detection of
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