ArticleIEEE sensors journal2020
Rapid antibiotic susceptibility testing based on bacterial motion patterns with long short-term memory neural networks.
Article in IEEE sensors journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Microfluidic technologies for advanced antimicrobial susceptibility testing.Biomicrofluidics · 2024Review
- Deep Learning-Based Culture-Free Bacteria Detection in Urine Using Large-Volume Microscopy.Biosensors · 2024Article
- Deep learning and single-cell phenotyping for rapid antimicrobial susceptibility detection in Escherichia coli.Communications biology · 2023Article
- Detection of antimicrobial impact on gram-negative bacterial cell envelope based on single-cell imaging by scanning electron microscopy.Scientific reports · 2023Article
- Single-cell pathogen diagnostics for combating antibiotic resistance.Nature reviews. Methods primers · 2023Article
- Label-free single-particle imaging approach for ultra-rapid detection of pathogenic bacteria in clinical samples.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Biomedical Microscopic Imaging in Computational Intelligence Using Deep Learning Ensemble Convolution Learning-Based Feature Extraction and Classification.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Antibiotic resistance is an increasing public health threat. To combat it, a fast method to determine the antibiotic susceptibility of infecting pathogens is required. Here we present an optical imaging-based method to track the motion of single bacterial cells and generate a model to classify active and inactive cells based on the motion patterns of the individual cells. The model includes an image-processing algorithm to segment individual bacterial cells and track the motion of the cells over time, and a deep learning algorithm (Long Short-Term Memory network) to learn and determine if a bacterial cell is active or inactive. By applying the model to human urine specimens spiked with an Escherichia coli lab strain, we show that the method can accurately perform antibiotic susceptibility testing as fast as 30 minutes for five commonly used antibiotics.
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Registered trials
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