Surface-enhanced Raman scattering (SERS) in antibiotic resistance detection: Advances, challenges, and future...

Area of Science:

• Optics and Photonics
• Biotechnology
• Artificial Intelligence

Background:

• Antimicrobial resistance (AMR) is a critical global health crisis, causing significant annual mortality.
• Early, rapid, and accurate detection of drug-resistant bacteria is essential for effective treatment and control.
• Surface-enhanced Raman scattering (SERS) offers a sensitive, label-free method for bacterial analysis.

Purpose of the Study:

• To review recent advances in SERS-AI combined strategies for AMR detection.
• To analyze the strengths and limitations of various SERS-AI approaches.
• To explore the potential clinical and surveillance applications of these integrated technologies.

Main Methods:

• Systematic review of literature on SERS combined with machine learning (ML) and deep learning (DL) for AMR detection.
• Analysis of SERS-AI methodologies, focusing on sensitivity, specificity, and speed.
• Evaluation of potential applications in clinical diagnostics and public health surveillance.

Main Results:

• SERS-AI strategies demonstrate high accuracy and efficiency in detecting and identifying drug-resistant bacteria.
• Integration of SERS with AI, particularly ML/DL, significantly enhances resistance detection capabilities.
• Various SERS-AI approaches show promise for rapid, label-free antimicrobial resistance analysis.

Conclusions:

• SERS-AI combined strategies represent a powerful tool for combating the AMR crisis.
• Further technological innovation and interdisciplinary collaboration are crucial for translational application.
• These integrated approaches hold significant potential for improving clinical diagnostics and global AMR surveillance.