ArticleCommunications biology2023
Deep learning and single-cell phenotyping for rapid antimicrobial susceptibility detection in Escherichia coli.
Article in Communications biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 47 citations in OpenAlex.
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- Artificial intelligence for improving decision-making in bacterial infection management: a narrative review.The Journal of antimicrobial chemotherapy · 2026Review
- Artificial Intelligence for Antimicrobial Resistance Detection and Prediction inInfection and drug resistance · 2026Review
- Exploring allosteric efflux pump inhibitors and the role of bacterial membrane potential in modulating drug resistance.Frontiers in microbiology · 2026Review
- A comprehensive review of AI innovations for tackling antimicrobial resistance.Le infezioni in medicina · 2026Review
- Antibiotic resistance inFrontiers in microbiology · 2026Review
- Harnessing droplet microfluidics and morphology-based deep learning for the label-free study of polymicrobial-phage interactions.Communications biology · 2025Article
- Bacterial Proteomics and Antibiotic Resistance Identification: Is Single-Cell Analysis a Worthwhile Pursuit?Pathogens (Basel, Switzerland) · 2025Review
- Review
- From Farm to Fork: Antimicrobial-Resistant Bacterial Pathogens in Livestock Production and the Food Chain.Veterinary sciences · 2025Review
- Bacterial strain type and TEM-1 enzyme allele impact antibiotic susceptibility distribution in monoclonal populations: a single-cell droplet approach.Royal Society open science · 2025Article
- Analytical techniques and molecular platforms for detection and surveillance of antimicrobial resistance: advancements of the past decade.3 Biotech · 2025Review
- How AI can help us beat AMR.npj antimicrobials and resistance · 2025Review
- Metagenomics as a Transformative Tool for Antibiotic Resistance Surveillance: Highlighting the Impact of Mobile Genetic Elements with a Focus on the Complex Role of Phages.Antibiotics (Basel, Switzerland) · 2025Review
- Ribosome phenotypes for rapid classification of antibiotic-susceptible and resistant strains of Escherichia coli.Communications biology · 2025Article
- Deep learning-driven bacterial cytological profiling to determine antimicrobial mechanisms inProceedings of the National Academy of Sciences of the United States of America · 2025Article
- Antibiotics change the population growth rate heterogeneity and morphology of bacteria.PLoS pathogens · 2025Article
- Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance.npj antimicrobials and resistance · 2025Review
- Advancing antibiotic discovery with bacterial cytological profiling: a high-throughput solution to antimicrobial resistance.Frontiers in microbiology · 2025Review
- Detecting antibiotic resistance: classical, molecular, advanced bioengineering, and AI-enhanced approaches.Frontiers in microbiology · 2025Review
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
Abstract
The rise of antimicrobial resistance (AMR) is one of the greatest public health challenges, already causing up to 1.2 million deaths annually and rising. Current culture-based turnaround times for bacterial identification in clinical samples and antimicrobial susceptibility testing (AST) are typically 18-24 h. We present a novel proof-of-concept methodological advance in susceptibility testing based on the deep-learning of single-cell specific morphological phenotypes directly associated with antimicrobial susceptibility in Escherichia coli. Our models can reliably (80% single-cell accuracy) classify untreated and treated susceptible cells for a lab-reference fully susceptible E. coli strain, across four antibiotics (ciprofloxacin, gentamicin, rifampicin and co-amoxiclav). For ciprofloxacin, we demonstrate our models reveal significant (p < 0.001) differences between bacterial cell populations affected and unaffected by antibiotic treatment, and show that given treatment with a fixed concentration of 10 mg/L over 30 min these phenotypic effects correlate with clinical susceptibility defined by established clinical breakpoints. Deploying our approach on cell populations from six E. coli strains obtained from human bloodstream infections with varying degrees of ciprofloxacin resistance and treated with a range of ciprofloxacin concentrations, we show single-cell phenotyping has the potential to provide equivalent information to growth-based AST assays, but in as little as 30 min.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.