ReviewJournal of clinical microbiology2023
The Use of Machine Learning for Image Analysis Artificial Intelligence in Clinical Microbiology.
Review in Journal of clinical microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- Clinical application of artificial intelligence technology in epilepsy.Acta epileptologica · 2026Review
- Article
- [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
- Advancing mold identification in the routine laboratory, performance of smartphone-based imaging and a newly developed convolutional neural network.Microbiology spectrum · 2026Article
- Radiomic Analysis as a Powerful Tool for Cytological Images of Benign Thyroid Nodules Treated by Thermal Radiofrequency Ablation.Bioengineering (Basel, Switzerland) · 2026Article
- Machine learning for personalized antimicrobial susceptibility breakpoints.The Journal of antimicrobial chemotherapy · 2026Article
- Application of artificial intelligence in geriatric infection: recent advances and prospects.Frontiers in cellular and infection microbiology · 2026Review
- Advancements in artificial intelligence for atopic dermatitis: diagnosis, treatment, and patient management.Annals of medicine · 2025Review
- Validation of Digital Slide Scanning and a Convolutional Neural Network for the Detection of Intestinal Parasites in Human Stool Samples.Diagnostics (Basel, Switzerland) · 2025Article
- Update on North American tick-borne diseases and how to diagnose them.Journal of clinical microbiology · 2025Review
- Article
- Proceedings of the Clinical Microbiology Open 2024: artificial intelligence applications in clinical microbiology.Journal of clinical microbiology · 2025Article
- The application of machine learning in clinical microbiology and infectious diseases.Frontiers in cellular and infection microbiology · 2025Review
- Can Artificial Intelligence Enhance the Antimicrobial Effectiveness of Photodynamic Therapy? What We Know and What We Do Not Know?Journal of lasers in medical sciences · 2025Review
- GPT-4-based AI agents-the new expert system for detection of antimicrobial resistance mechanisms?Journal of clinical microbiology · 2024Article
- Laboratory Preparation for Digital Medicine in Healthcare 4.0: An Investigation Into the Awareness and Applications of Big Data and Artificial Intelligence.Annals of laboratory medicine · 2024Article
- NeurostimML: a machine learning model for predicting neurostimulation-induced tissue damage.Journal of neural engineering · 2024Article
- The Impact of Artificial Intelligence on Microbial Diagnosis.Microorganisms · 2024Review
- 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
- Application of Nanopore Sequencing in the Diagnosis and Treatment of Pulmonary Infections.Molecular diagnosis & therapy · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The growing transition to digital microbiology in clinical laboratories creates the opportunity to interpret images using software. Software analysis tools can be designed to use human-curated knowledge and expert rules, but more novel artificial intelligence (AI) approaches such as machine learning (ML) are being integrated into clinical microbiology practice. These image analysis AI (IAAI) tools are beginning to penetrate routine clinical microbiology practice, and their scope and impact on routine clinical microbiology practice will continue to grow. This review separates the IAAI applications into 2 broad classification categories: (i) rare event detection/classification or (ii) score-based/categorical classification. Rare event detection can be used for screening purposes or for final identification of a microbe including microscopic detection of mycobacteria in a primary specimen, detection of bacterial colonies growing on nutrient agar, or detection of parasites in a stool preparation or blood smear. Score-based image analysis can be applied to a scoring system that classifies images
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Identifiers
What OpenQuestion holds
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.