ArticleNature communications2023
Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Evaluation of an AI-Assisted Colony Counting System Across Multiple Culture Media Using Standardized Pure Culture Plates.Microorganisms · 2026Article
- Comparative evaluation of deep learning architectures for microbial colony classification in microbiological imaging.BMC microbiology · 2026Article
- High-Resolution Colony Images of Clinically Isolated Bacteria for Automated Detection and Deep Learning.Scientific data · 2026Article
- The evolution of diagnostic microbiology: integrating culture-based methods and genomic advances.PeerJ · 2026Review
- The Application of artificial intelligence in periprosthetic joint infection.Journal of advanced research · 2026Review
- Uncloaking the black-box: the need for explainable artificial intelligence in clinical microbiology and infectious diseases applications.Frontiers in public health · 2026Review
- From microscopy to antimicrobial decisions: a clinically grounded roadmap for critical care infectious diseases.Frontiers in artificial intelligence · 2026Review
- Article
- Implementation of convolutional neural networks for microbial colony recognition.Microbiology spectrum · 2025Article
- Evaluation of automated streaking patterns in urine culture for clinical workflow optimization.Scientific reports · 2025Article
- Proceedings of the Clinical Microbiology Open 2024: artificial intelligence applications in clinical microbiology.Journal of clinical microbiology · 2025Article
- [PSI]-CIC: A Deep-Learning Pipeline for the Annotation of Sectored Saccharomyces cerevisiae Colonies.Bulletin of mathematical biology · 2024Article
- Improving the odds: Artificial intelligence and the great plate count anomaly.Microbial biotechnology · 2024Article
- A Clinical Bacterial Dataset for Deep Learning in Microbiological Rapid On-Site Evaluation.Scientific data · 2024Article
- Development and evaluation of an artificial intelligence for bacterial growth monitoring in clinical bacteriology.Journal of clinical microbiology · 2024Article
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Authors and funding
6 authors.
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Abstract
Full Laboratory Automation is revolutionizing work habits in an increasing number of clinical microbiology facilities worldwide, generating huge streams of digital images for interpretation. Contextually, deep learning architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic bacterial culture plates, including presumptive pathogen identification. This is achieved by decomposing the problem into a hierarchy of complex subtasks and addressing them with a multi-network architecture we call DeepColony. Working on a large stream of clinical data and a complete set of 32 pathogens, the proposed system is capable of effectively assist plate interpretation with a surprising degree of accuracy in the widespread and demanding framework of Urinary Tract Infections. Moreover, thanks to the rich species-related generated information, DeepColony can be used for developing trustworthy clinical decision support services in laboratory automation ecosystems from local to global scale.
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