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Using Extraordinary Optical Transmission to Quantify Cardiac Biomarkers in Human Serum
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Published on: December 13, 2017

AI-Assisted Surface-Enhanced Raman Spectroscopy for Cardiovascular Diagnostics: From Plasmonic Materials to Clinical

Anju Joshi1, Gymama Slaughter1,2

  • 1Center for Bioelectronics, Old Dominion University, Norfolk, VA 23508, USA.

Nanomaterials (Basel, Switzerland)
|July 13, 2026
PubMed
Summary

AI-assisted Surface-Enhanced Raman Spectroscopy (SERS) offers ultrasensitive cardiac biomarker detection. This review synthesizes AI-SERS platforms for cardiovascular diagnostics, addressing challenges for clinical translation.

Keywords:
artificial intelligencecardiac biomarkerscardiovascular diseasesurface-enhanced Raman spectroscopy

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Area of Science:

  • Biomedical Engineering
  • Analytical Chemistry
  • Artificial Intelligence

Background:

  • Surface-Enhanced Raman Spectroscopy (SERS) provides molecular fingerprint specificity for ultrasensitive cardiac biomarker detection.
  • Advances in plasmonic nanostructures and flexible platforms enhance SERS biosensor performance.
  • Artificial Intelligence (AI) and machine learning improve spectral data interpretation for biomarker classification and diagnostic accuracy.

Purpose of the Study:

  • To provide a comprehensive synthesis of AI-assisted SERS platforms for cardiovascular diagnostics.
  • To integrate advances in plasmonic materials, biomolecular recognition, and intelligent spectral analysis.
  • To examine translational barriers and outline future directions for SERS-AI systems.

Main Methods:

  • Review of recent literature on AI-assisted SERS for cardiovascular diagnostics.
  • Synthesis of progress in plasmonic materials, surface functionalization, and flexible sensing.
  • Analysis of AI/machine learning integration for spectral data interpretation and biomarker classification.

Main Results:

  • AI-SERS platforms demonstrate significant potential for sensitive and specific cardiac biomarker detection.
  • Integration of AI enhances diagnostic accuracy in complex biological samples.
  • Challenges remain in standardization, reproducibility, and clinical validation.

Conclusions:

  • AI-assisted SERS holds promise for advanced cardiovascular diagnostics.
  • Addressing translational barriers like data variability and model interpretability is crucial.
  • Future development should focus on standardized, deployable, and clinically validated SERS-AI systems.