ArticleJournal of clinical microbiology2018
Automated Interpretation of Blood Culture Gram Stains by Use of a Deep Convolutional Neural Network.
Article in Journal of clinical microbiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 1 of them a synthesis that pooled it.
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Who cites it
68 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Harnessing AI for advancing pathogenic microbiology: a bibliometric and topic modeling approach.Frontiers in microbiology · 2024Pooled it
- Artificial Intelligence in Infectious Diseases and Clinical Microbiology.Infectious diseases & clinical microbiology · 2026Review
- Comparison of Gram stain interpretation accuracy between a computer-aided diagnosis app and microbiology specialists in blood culture samples.Microbiology spectrum · 2026Observational
- From Gram Stain to Decision Support: Performance of Multimodal Large Language Models in Blood Culture Microscopy.Journal of imaging informatics in medicine · 2026Article
- Gram staining decipherment using an artificial intelligence-powered smartphone-based application.Microbiology spectrum · 2026Observational
- Digital holographic microscopy: use of artificial intelligence to enable rapid blood culture Gram stain digitization and interpretation.Journal of clinical microbiology · 2026Article
- Artificial intelligence in microbiology: implications for metagenomics, diagnostics, and AMR surveillance.Biomedical engineering online · 2026Review
- Identification ofJournal of clinical microbiology · 2026Article
- Advancing mold identification in the routine laboratory, performance of smartphone-based imaging and a newly developed convolutional neural network.Microbiology spectrum · 2026Article
- An annotated dataset of Gram stains from positive blood cultures.Scientific data · 2026Article
- What's not to learn? AI meets parasitology.Journal of clinical microbiology · 2026Article
- Artificial intelligence for improving decision-making in bacterial infection management: a narrative review.The Journal of antimicrobial chemotherapy · 2026Review
- Uncloaking the black-box: the need for explainable artificial intelligence in clinical microbiology and infectious diseases applications.Frontiers in public health · 2026Review
- Application of few-shot learning and transfer learning based on YOLOv6 in the recognition of bacteria in sputum M-ROSE.Frontiers in cellular and infection microbiology · 2026Article
- Evolving microbiology laboratories: mastering automated culture-based processes and molecular assays, an institutional experience.Frontiers in cellular and infection microbiology · 2026Review
- From microscopy to antimicrobial decisions: a clinically grounded roadmap for critical care infectious diseases.Frontiers in artificial intelligence · 2026Review
- The evolution of diagnostic microbiology: integrating culture-based methods and genomic advances.PeerJ · 2026Review
- Building the next frontier: Artificial intelligence in 3D-printed medicines.Biomaterials translational · 2026Review
- Validation of Digital Slide Scanning and a Convolutional Neural Network for the Detection of Intestinal Parasites in Human Stool Samples.Diagnostics (Basel, Switzerland) · 2025Article
- Implementation of convolutional neural networks for microbial colony recognition.Microbiology spectrum · 2025Article
8 more citing papers are in PubMed but not listed here.
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3 authors.
Funding
Abstract
Microscopic interpretation of stained smears is one of the most operator-dependent and time-intensive activities in the clinical microbiology laboratory. Here, we investigated application of an automated image acquisition and convolutional neural network (CNN)-based approach for automated Gram stain classification. Using an automated microscopy platform, uncoverslipped slides were scanned with a 40× dry objective, generating images of sufficient resolution for interpretation. We collected 25,488 images from positive blood culture Gram stains prepared during routine clinical workup. These images were used to generate 100,213 crops containing Gram-positive cocci in clusters, Gram-positive cocci in chains/pairs, Gram-negative rods, or background (no cells). These categories were targeted for proof-of-concept development as they are associated with the majority of bloodstream infections. Our CNN model achieved a classification accuracy of 94.9% on a test set of image crops. Receiver operating characteristic (ROC) curve analysis indicated a robust ability to differentiate between categories with an area under the curve of >0.98 for each. After training and validation, we applied the classification algorithm to new images collected from 189 whole slides without human intervention. Sensitivity and specificity were 98.4% and 75.0% for Gram-positive cocci in chains and pairs, 93.2% and 97.2% for Gram-positive cocci in clusters, and 96.3% and 98.1% for Gram-negative rods. Taken together, our data support a proof of concept for a fully automated classification methodology for blood-culture Gram stains. Importantly, the algorithm was highly adept at identifying image crops with organisms and could be used to present prescreened, classified crops to technologists to accelerate smear review. This concept could potentially be extended to all Gram stain interpretive activities in the clinical laboratory.
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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.