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.

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms

“…Thus, it is an overstatement to consider the staphyloxanthin bands as a biomarker for the detection of methicillin resistance, especially since this molecule is a virulence factor and does not have any relation to the methicillin-resistance mechanism [ 51 ]. Another option for these types of strains could be label-free or label-based SERS, that allows the amplification of signals from specific bacterial molecules at low intercellular concentrations or without characteristic Raman signatures [ 37 , 52 ].…”

“…In the second case, the SERS tag is bound to the bacteria surface via antibodies or aptamers and the specific label inside the used SERS tag then gives the spectral information to identify this bacterium [20,[34][35][36]. For a more detailed description of SERS studies on bacteria please refer to references [29][30][31][32][33][37][38][39][40][41][42], since this manuscript is manly dedicated to conventional Raman spectroscopic studies.…”

Synergizing Nanomaterials and Artificial Intelligence in Advanced Optical Biosensors for Precision Antimicrobial Resistance Diagnosis

“…The article highlights ongoing research efforts and their revolutionary influence on public health protection as it explores the synergy between SERS and AI, specifically ML and DL. 154 Rapid and effective detection methods are urgently required due to the alarming increase in infectious infections, which are responsible for countless fatalities worldwide. Accurate diagnosis of microbial infections is essential for effective treatment; optical biosensors provide a user-friendly, multiplexed, and extremely sensitive approach.…”

“…It demonstrates how surface-enhanced Raman spectroscopy (SERS) combined with AI can identify and characterize drug-resistant bacterial populations early. The article highlights ongoing research efforts and their revolutionary influence on public health protection as it explores the synergy between SERS and AI, specifically ML and DL . Rapid and effective detection methods are urgently required due to the alarming increase in infectious infections, which are responsible for countless fatalities worldwide.…”

Enhancing Cookie Formulations with Combined Dehydrated Peach: A Machine Learning Approach for Technological Quality Assessment and Optimization

“…Adaptable non-linear methods serve as powerful tools for modeling intricate relationships in diverse datasets from instrumental analysis and are extensively employed in food analysis for classification, optimization, and regression investigations; numerous examples demonstrate their ability to achieve high-quality results that surpass those available from traditional methods in some cases [ 18 , 19 ].…”