Akademik Veri Yönetim Sistemi
ADVANCED MATERIALS INTERFACES, 2023 (SCI-Expanded)
Antimicrobial resistance (AMR) is a global crisis, responsible for approximate to 700 000 annual deaths, as reported by the World Health Organization. To counteract this growing threat to public health, innovative solutions for early detection and characterization of drug-resistant bacterial strains are imperative. Surface-enhanced Raman spectroscopy (SERS) combined with artificial intelligence (AI) technology presents a promising avenue to address this challenge. This review provides a concise overview of the latest advancements in SERS and AI, showcasing their transformative potential in the context of AMR. It explores the diverse methodologies proposed, highlighting their advantages and limitations. Additionally, the review underscores the significance of SERS in tandem use with machine learning (ML) and deep learning (DL) in combating AMR and emphasizes the importance of ongoing research and development efforts in this critical field. Future developments for this technology could transform the way antimicrobial resistance (AMR) is addressed and pave the way for novel approaches to the protection of public health worldwide. This review discusses the use of Surface-Enhanced Raman Spectroscopy (SERS) combined with Artificial Intelligence (AI) for detecting antimicrobial resistance (AMR). Various SERS studies used with AI techniques, including machine learning and deep learning, are analyzed for their advantages and limitations. The review emphasizes the importance of ongoing research to combat the critical challenge of AMR.image