Artificial Intelligence-Driven Image Analysis of Bacterial Cells and Biofilms

Artificial Intelligence-Driven Analysis of Antimicrobial-Resistant and Biofilm-Forming Pathogens on Biotic and Abiotic Surfaces

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.

Tools of the Trade: Image Analysis Programs for Confocal Laser-Scanning Microscopy Studies of Biofilms and Considerations for Their Use by Experimental Researchers

Confocal laser-scanning microscopy (CLSM) is the bedrock of the microscopic visualization of biofilms. Previous applications of CLSM in biofilm studies have largely focused on observations of bacterial or fungal elements of biofilms, often seen as aggregates or mats of cells. However, the field of biofilm research is moving beyond qualitative observations alone, toward the quantitative analysis of the structural and functional features of biofilms, across clinical, environmental, and laboratory conditions. In recent times, several image analysis programs have been developed to extract and quantify biofilm properties from confocal micrographs. These tools not only vary in their scope and relevance to the specific biofilm features under study but also with respect to the user interface, compatibility with operating systems, and raw image requirements. Understanding these considerations is important when selecting tools for quantitative biofilm analysis, including at the initial experimental stages of image acquisition. In this review, we provide an overview of image analysis programs for confocal micrographs of biofilms, with a focus on tool selection and image acquisition parameters that are relevant for experimental researchers to ensure reliability and compatibility with downstream image processing.

Biofilms in Surgical Site Infections: Recent Advances and Novel Prevention and Eradication Strategies

Surgical site infections (SSIs) are common postoperative occurrences due to contamination of the surgical wound or implanted medical devices with community or hospital-acquired microorganisms, as well as other endogenous opportunistic microbes. Despite numerous rules and guidelines applied to prevent these infections, SSI rates are considerably high, constituting a threat to the healthcare system in terms of morbidity, prolonged hospitalization, and death. Approximately 80% of human SSIs, including chronic wound infections, are related to biofilm-forming bacteria. Biofilm-associated SSIs are extremely difficult to treat with conventional antibiotics due to several tolerance mechanisms provided by the multidrug-resistant bacteria, usually arranged as polymicrobial communities. In this review, novel strategies to control, i.e., prevent and eradicate, biofilms in SSIs are presented and discussed, focusing mainly on two attractive approaches: the use of nanotechnology-based composites and natural plant-based products. An overview of new therapeutic agents and strategic approaches to control epidemic multidrug-resistant pathogenic microorganisms, particularly when biofilms are present, is provided alongside other combinatorial approaches as attempts to obtain synergistic effects with conventional antibiotics and restore their efficacy to treat biofilm-mediated SSIs. Some detection and real-time monitoring systems to improve biofilm control strategies and diagnosis of human infections are also discussed.