ReviewAntibiotics (Basel, Switzerland)2024
Artificial Intelligence-Driven Analysis of Antimicrobial-Resistant and Biofilm-Forming Pathogens on Biotic and Abiotic Surfaces.
Review in Antibiotics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Identification of acylhydrazone derivatives with isopropanolamine moiety as potent anti-virulence agents for combating bacterial infection.Pest management science · 2026Article
- Article
- Cytokine and chemokine dysregulation by microbial biofilm components: mechanisms and clinical implications.Archives of microbiology · 2026Review
- Targeting biofilm-driven antibiotic resistance: emerging mechanisms and next-generation therapeutic interventions.Frontiers in microbiology · 2026Review
- Artificial intelligence applications in biofilm research: computational approaches for biofilm analysis and antimicrobial prediction.Frontiers in microbiology · 2026Review
- Interconnected worlds: a comprehensive review of fungal defenses, antimicrobial resistance, and their evolutionary dynamics.IMA fungus · 2026Review
- Review
- Review
- New Methodologies as Opportunities in the Study of Bacterial Biofilms, Including Food-Related Applications.Microorganisms · 2025Review
- Review
- An Integrative Framework for Healthcare Recommendation Systems: Leveraging the Linear Discriminant Wolf-Convolutional Neural Network (LDW-CNN) Model.Diagnostics (Basel, Switzerland) · 2024Article
- Anti-Antibiotics (Basel, Switzerland) · 2024Article
- Detection of Mycotoxin Contamination in Foods Using Artificial Intelligence: A Review.Foods (Basel, Switzerland) · 2024Review
- The clinical prediction model to distinguish between colonization and infection byFrontiers in microbiology · 2024Article
- Leveraging artificial intelligence to combat antimicrobial resistance in geriatric care.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The growing threat of antimicrobial-resistant (AMR) pathogens to human health worldwide emphasizes the need for more effective infection control strategies. Bacterial and fungal biofilms pose a major challenge in treating AMR pathogen infections. Biofilms are formed by pathogenic microbes encased in extracellular polymeric substances to confer protection from antimicrobials and the host immune system. Biofilms also promote the growth of antibiotic-resistant mutants and latent persister cells and thus complicate therapeutic approaches. Biofilms are ubiquitous and cause serious health risks due to their ability to colonize various surfaces, including human tissues, medical devices, and food-processing equipment. Detection and characterization of biofilms are crucial for prompt intervention and infection control. To this end, traditional approaches are often effective, yet they fail to identify the microbial species inside biofilms. Recent advances in artificial intelligence (AI) have provided new avenues to improve biofilm identification. Machine-learning algorithms and image-processing techniques have shown promise for the accurate and efficient detection of biofilm-forming microorganisms on biotic and abiotic surfaces. These advancements have the potential to transform biofilm research and clinical practice by allowing faster diagnosis and more tailored therapy. This comprehensive review focuses on the application of AI techniques for the identification of biofilm-forming pathogens in various industries, including healthcare, food safety, and agriculture. The review discusses the existing approaches, challenges, and potential applications of AI in biofilm research, with a particular focus on the role of AI in improving diagnostic capacities and guiding preventative actions. The synthesis of the current knowledge and future directions, as described in this review, will guide future research and development efforts in combating biofilm-associated infections.
Indexed as
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.