Decoding biochemical fingerprints: Artificial intelligence-empowered Raman spectroscopy for rapid phenotypic...

Area of Science:

• Biomedical Engineering
• Analytical Chemistry
• Computational Biology

Background:

• Antimicrobial resistance (AMR) necessitates rapid phenotypic antimicrobial susceptibility testing (AST).
• Raman spectroscopy offers label-free biochemical fingerprinting of pathogens but faces challenges in clinical samples (low SNR, background fluorescence, batch variation).
• Artificial intelligence (AI), especially deep learning, presents a solution to overcome these analytical bottlenecks.

Purpose of the Study:

• To propose a synergistic framework for AI-assisted Raman AST, integrating hardware and algorithms.
• To systematically evaluate trade-offs in Raman platforms (RS, SERS, LTRS) and AI architectures (CNNs, Transformers, transfer learning).
• To discuss advancements like explainable AI, closed-loop systems, and future directions for clinical translation.

Main Methods:

• Systematic assessment of physical trade-offs among different Raman spectroscopy platforms.
• Analysis of feature-learning mechanisms in various AI architectures, including deep convolutional networks and Transformers.
• Discussion of explainable AI, microfluidics integration, and Raman-activated cell sorting (RACS).

Main Results:

• Transformers show potential for capturing long-range dependencies but pose computational overhead for Point-of-Care Testing (POCT).
• The review outlines a framework for synergistic hardware-algorithm evaluation in AI-assisted Raman AST.
• Identified key challenges and potential solutions for clinical implementation of rapid AST.

Conclusions:

• A hardware-algorithm synergistic approach is crucial for advancing AI-assisted Raman AST.
• Explainable AI and integrated systems (microfluidics, RACS) are vital for clinical diagnostics.
• Future directions like foundation models and multi-modal fusion aim to overcome translational barriers for rapid AST.