ArticlePLoS computational biology2023
Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 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
- Deep Learning Integration in Optical Microscopy: Advancements and Applications.Microscopy research and technique · 2026Review
- OsteoNet: A deep learning framework linking cellular morphology to molecular markers for quantifying osteogenic differentiation.Computational and structural biotechnology journal · 2026Article
- Artificial intelligence in biology and medicine.Die Naturwissenschaften · 2025Review
- Article
- Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy.PLoS computational biology · 2023Article
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Authors and funding
5 authors.
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Abstract
Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. For instance, identifying the species and its antibiotic susceptibility is vital for effective bacterial infection treatment. Here we show that phase contrast time-lapse microscopy combined with deep learning is sufficient to classify four species of bacteria relevant to human health. The classification is performed on living bacteria and does not require fixation or staining, meaning that the bacterial species can be determined as the bacteria reproduce in a microfluidic device, enabling parallel determination of susceptibility to antibiotics. We assess the performance of convolutional neural networks and vision transformers, where the best model attained a class-average accuracy exceeding 98%. Our successful proof-of-principle results suggest that the methods should be challenged with data covering more species and clinically relevant isolates for future clinical use.
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Registered trials
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