Using Machine Learning and Silver Nanoparticle-Based Surface-Enhanced Raman Spectroscopy for Classification of Cardiovascular Disease Biomarkers

Katelyn Dixon; Raissa Bonon; Felix Ivander; Saba Ale Ebrahim; Khashayar Namdar; Moein Shayegannia; Farzad Khalvati; Nazir P Kherani; Anna Zavodni; Naomi Matsuura

doi:10.1021/acsanm.3c01442

Using Machine Learning and Silver Nanoparticle-Based Surface-Enhanced Raman Spectroscopy for Classification of Cardiovascular Disease Biomarkers

ACS Appl Nano Mater. 2023 Aug 22;6(17):15385-15396. doi: 10.1021/acsanm.3c01442. eCollection 2023 Sep 8.

Authors

Katelyn Dixon¹, Raissa Bonon², Felix Ivander², Saba Ale Ebrahim¹, Khashayar Namdar³, Moein Shayegannia¹, Farzad Khalvati^{3

4

5

6

7}, Nazir P Kherani^{1

8}, Anna Zavodni⁹, Naomi Matsuura^{2

8

4}

Affiliations

¹ Department of Electrical and Computer Engineering, University of Toronto, Toronto M5S 1A4, Canada.
² Institute of Biomedical Engineering, University of Toronto, Toronto M5S 3E2, Canada.
³ Institute of Medical Science, University of Toronto, Toronto M5S 1A8, Canada.
⁴ Department of Medical Imaging, University of Toronto, Toronto M5T 1W7, Canada.
⁵ The Hospital for Sick Children, Toronto, Ontario M5G 1E8, Canada.
⁶ Department of Computer Science, University of Toronto, Toronto M5S 2E4, Canada.
⁷ Department of Mechanical and Industrial Engineering, University of Toronto, Toronto M5S 3G8, Canada.
⁸ Department of Materials Science and Engineering, University of Toronto, Toronto M5S 3E4, Canada.
⁹ Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto M5T 1W7, Canada.

PMID: 37706067
PMCID: PMC10496841 (available on 2024-08-22)
DOI: 10.1021/acsanm.3c01442

Abstract

Characterizing complex biofluids using surface-enhanced Raman spectroscopy (SERS) coupled with machine learning (ML) has been proposed as a powerful tool for point-of-care detection of clinical disease. ML is well-suited to categorizing otherwise uninterpretable, patient-derived SERS spectra that contain a multitude of low concentration, disease-specific molecular biomarkers among a dense spectral background of biological molecules. However, ML can generate false, non-generalizable models when data sets used for model training are inadequate. It is thus critical to determine how different SERS experimental methodologies and workflow parameters can potentially impact ML disease classification of clinical samples. In this study, a label-free, broadband, Ag nanoparticle-based SERS platform was coupled with ML to assess simulated clinical samples for cardiovascular disease (CVD), containing randomized combinations of five key CVD biomarkers at clinically relevant concentrations in serum. Raman spectra obtained at 532, 633, and 785 nm from up to 300 unique samples were classified into physiological and pathological categories using two standard ML models. Label-free SERS and ML could correctly classify randomized CVD samples with high accuracies of up to 90.0% at 532 nm using as few as 200 training samples. Spectra obtained at 532 nm produced the highest accuracies with no significant increase achieved using multiwavelength SERS. Sample preparation and measurement methodologies (e.g., different SERS substrate lots, sample volumes, sample sizes, and known variations in randomization and experimental handling) were shown to strongly influence the ML classification and could artificially increase classification accuracies by as much as 27%. This detailed investigation into the proper application of ML techniques for CVD classification can lead to improved data set acquisition required for the SERS community, such that ML on labeled and robust SERS data sets can be practically applied for future point-of-care testing in patients.