ReviewClinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases2020
Image analysis and artificial intelligence in infectious disease diagnostics.
Review in Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 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
40 citing papers in PubMed.
- [Advances in the Application of Artificial Intelligence in Clinical Microbiological Testing].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Review
- Novel convolutional neural network for bacterial identification of confocal microscopic datasets.Scientific reports · 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
- 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
- Proceedings of the Clinical Microbiology Open 2024: artificial intelligence applications in clinical microbiology.Journal of clinical microbiology · 2025Article
- Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivax.Scientific reports · 2025Article
- Artificial Intelligence and Large Language Models in the Fight Against Superficial Fungal Infections: Friend or Foe?Clinical, cosmetic and investigational dermatology · 2025Review
- AI-driven strategies for advancing corneal cell therapy: a promising frontier.Frontiers in medicine · 2025Review
- Evaluation of an automatic image classifier for analysis of bacterial growth on a multiple-agar plate developed for bovine mastitis.PloS one · 2025Article
- Artificial intelligence applications for immunology laboratory: image analysis and classification study of IIF photos.Immunologic research · 2024Article
- Validation of Vetscan ImagystParasites & vectors · 2024Article
- Artificial intelligence performance in testing microfluidics for point-of-care.Lab on a chip · 2024Article
- Review
- Validation of Serum Calprotectin Relative to Other Biomarkers of Infection in Febrile Infants Presenting to the Emergency Department.Antibiotics (Basel, Switzerland) · 2024Article
- Retrospective validation of MetaSystems' deep-learning-based digital microscopy platform with assistance compared to manual fluorescence microscopy for detection of mycobacteria.Journal of clinical microbiology · 2024Article
- Confronting the Disruption of the Infectious Diseases Workforce by Artificial Intelligence: What This Means for Us and What We Can Do About It.Open forum infectious diseases · 2024Review
- Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundMicrobiologists are valued for their time-honed skills in image analysis, including identification of pathogens and inflammatory context in Gram stains, ova and parasite preparations, blood smears and histopathologic slides. They also must classify colony growth on a variety of agar plates for triage and assessment. Recent advances in image analysis, in particular application of artificial intelligence (AI), have the potential to automate these processes and support more timely and accurate diagnoses.
objectivesTo review current AI-based image analysis as applied to clinical microbiology; and to discuss future trends in the field. SOURCES: Material sourced for this review included peer-reviewed literature annotated in the PubMed or Google Scholar databases and preprint articles from bioRxiv. Articles describing use of AI for analysis of images used in infectious disease diagnostics were reviewed. CONTENT: We describe application of machine learning towards analysis of different types of microbiologic image data. Specifically, we outline progress in smear and plate interpretation as well as the potential for AI diagnostic applications in the clinical microbiology laboratory. IMPLICATIONS: Combined with automation, we predict that AI algorithms will be used in the future to prescreen and preclassify image data, thereby increasing productivity and enabling more accurate diagnoses through collaboration between the AI and the microbiologist. Once developed, image-based AI analysis is inexpensive and amenable to local and remote diagnostic use.
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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.