TY - JOUR
T1 - SERS spectroscopy with machine learning to analyze human plasma derived sEVs for coronary artery disease diagnosis and prognosis
AU - Huang, Xi
AU - Liu, Bo
AU - Guo, Shenghan
AU - Guo, Weihong
AU - Liao, Ke
AU - Hu, Guoku
AU - Shi, Wen
AU - Kuss, Mitchell
AU - Duryee, Michael J.
AU - Anderson, Daniel R.
AU - Lu, Yongfeng
AU - Duan, Bin
N1 - Publisher Copyright: © 2022 The Authors. Bioengineering & Translational Medicine published by Wiley Periodicals LLC on behalf of American Institute of Chemical Engineers.
PY - 2023/3
Y1 - 2023/3
N2 - Coronary artery disease (CAD) is one of the major cardiovascular diseases and represents the leading causes of global mortality. Developing new diagnostic and therapeutic approaches for CAD treatment are critically needed, especially for an early accurate CAD detection and further timely intervention. In this study, we successfully isolated human plasma small extracellular vesicles (sEVs) from four stages of CAD patients, that is, healthy control, stable plaque, non-ST-elevation myocardial infarction, and ST-elevation myocardial infarction. Surface-enhanced Raman scattering (SERS) measurement in conjunction with five machine learning approaches, including Quadratic Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor, Artificial Neural network, were then applied for the classification and prediction of the sEV samples. Among these five approaches, the overall accuracy of SVM shows the best predication results on both early CAD detection (86.4%) and overall prediction (92.3%). SVM also possesses the highest sensitivity (97.69%) and specificity (95.7%). Thus, our study demonstrates a promising strategy for noninvasive, safe, and high accurate diagnosis for CAD early detection.
AB - Coronary artery disease (CAD) is one of the major cardiovascular diseases and represents the leading causes of global mortality. Developing new diagnostic and therapeutic approaches for CAD treatment are critically needed, especially for an early accurate CAD detection and further timely intervention. In this study, we successfully isolated human plasma small extracellular vesicles (sEVs) from four stages of CAD patients, that is, healthy control, stable plaque, non-ST-elevation myocardial infarction, and ST-elevation myocardial infarction. Surface-enhanced Raman scattering (SERS) measurement in conjunction with five machine learning approaches, including Quadratic Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor, Artificial Neural network, were then applied for the classification and prediction of the sEV samples. Among these five approaches, the overall accuracy of SVM shows the best predication results on both early CAD detection (86.4%) and overall prediction (92.3%). SVM also possesses the highest sensitivity (97.69%) and specificity (95.7%). Thus, our study demonstrates a promising strategy for noninvasive, safe, and high accurate diagnosis for CAD early detection.
KW - coronary artery disease
KW - diagnostics
KW - machine learning
KW - small extracellular vesicles
KW - spectrogram
UR - http://www.scopus.com/inward/record.url?scp=85139389437&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85139389437&partnerID=8YFLogxK
U2 - 10.1002/btm2.10420
DO - 10.1002/btm2.10420
M3 - Article
SN - 2380-6761
VL - 8
JO - Bioengineering and Translational Medicine
JF - Bioengineering and Translational Medicine
IS - 2
M1 - e10420
ER -