Breakthrough Solution for Antimicrobial Resistance Detection: Surface-Enhanced Raman Spectroscopy-based on Artificial Intelligence

Abstract

Antimicrobial resistance (AMR) is a global crisis, responsible for ≈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.