Mishra Akanksha, Tabassum Nazia, Aggarwal Ashish, Kim Young-Mog, Khan Fazlurrahman
School of Bioengineering and Biosciences, Lovely Professional University, Phagwara 144001, Punjab, India.
Marine Integrated Biomedical Technology Center, The National Key Research Institutes in Universities, Pukyong National University, Busan 48513, Republic of Korea.
Antibiotics (Basel). 2024 Aug 22;13(8):788. doi: 10.3390/antibiotics13080788.
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.
全球范围内,抗微生物药物耐药性(AMR)病原体对人类健康构成的威胁日益严重,这凸显了采取更有效感染控制策略的必要性。细菌和真菌生物膜在治疗AMR病原体感染方面构成了重大挑战。生物膜由包裹在细胞外聚合物中的致病微生物形成,可抵御抗菌药物和宿主免疫系统。生物膜还促进抗生素耐药突变体和潜伏性持留菌的生长,从而使治疗方法变得复杂。生物膜无处不在,由于其能够在包括人体组织、医疗设备和食品加工设备在内的各种表面定殖,会导致严重的健康风险。生物膜的检测和表征对于及时干预和感染控制至关重要。为此,传统方法通常有效,但它们无法识别生物膜内的微生物种类。人工智能(AI)的最新进展为改善生物膜识别提供了新途径。机器学习算法和图像处理技术已显示出在准确高效检测生物和非生物表面上形成生物膜的微生物方面的潜力。这些进展有可能通过实现更快的诊断和更具针对性的治疗来改变生物膜研究和临床实践。这篇综述重点关注AI技术在包括医疗保健、食品安全和农业在内的各个行业中用于识别形成生物膜的病原体的应用。该综述讨论了AI在生物膜研究中的现有方法、挑战和潜在应用,特别关注AI在提高诊断能力和指导预防行动方面的作用。如本综述所述,对当前知识和未来方向的综合将指导未来对抗生物膜相关感染的研发工作。