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Raman spectroscopy combined with machine learning and chemometrics analyses as a tool for identification
Jan Jakub Kęsik1, Wiesław Paja2, Piotr Terlecki1
1Department of Vascular Surgery and Angiology, Medical University of Lublin, Poland.
Insights
Raman spectroscopy can detect atherosclerotic carotid stenosis (ACS) using serum. This method identifies spectral changes, offering a potential diagnostic tool for early disease detection before stroke occurs.
Area of Science:
- Biomedical Spectroscopy
- Medical Diagnostics
- Analytical Chemistry
Background:
- Atherosclerotic carotid stenosis (ACS) is a primary cause of stroke.
- ACS often presents with advanced disease or post-stroke due to asymptomatic progression.
- Early detection via routine tests is crucial for timely intervention.
Purpose of the Study:
- To evaluate Raman spectroscopy as a non-invasive diagnostic method for ACS.
- To identify specific spectral markers in serum indicative of ACS.
- To assess the diagnostic performance of Raman spectroscopy using machine learning.
Main Methods:
- Serum samples from ACS patients and controls were analyzed using Raman spectroscopy.
- Spectral data were analyzed using Principal Component Analysis (PCA) and machine learning algorithms.
- Decision tree algorithms identified potential spectral markers.
Main Results:
- Distinct Raman spectral differences were observed between ACS and control groups.
- Key spectral changes included decreased peaks around 1520 cm⁻¹ and increased peaks around 3050 cm⁻¹.
- Machine learning models achieved >90% accuracy, sensitivity, and selectivity, with AUC-ROC values up to 0.86.
Conclusions:
- Raman spectroscopy effectively distinguishes serum from ACS patients and controls.
- Specific spectral peaks and shifts serve as potential biomarkers for ACS detection.
- Raman spectroscopy shows significant potential for early, non-invasive diagnosis of atherosclerotic carotid stenosis.
Abstract:
Atherosclerosis carotid stenosis (ACS) is one of the main causes of stroke. Unfortunately, the highest number of people go to the doctor with an advanced disease or as a result of a stroke, because carotid atherosclerosis does not cause obvious symptoms. Therefore, it is important to find a diagnostic method to detect the disease during routine tests (using blood or serum). Consequently, in this article, Raman spectroscopy was tested as a potential diagnostic method. Indeed, Raman spectra of serum collected from ACS and control patients showed decrease of Raman peak around 1520 cm-1 and increase of peak around 3050 cm-1 in people with ACS. Moreover in people with ACS shift of peaks originating from amides II, I and lipids vibrations were noticed in comparison with control group. Interestingly, decision tree algorithm showed that peaks at 1656 cm-1 and 2957 cm-1 could be a spectroscopy markers of atherosclerotic carotid stenosis. Continuing, Principal Component Analysis (PCA) clearly showed distinguishing between serum collected from ACS and control patients, while machine learning algorithms showed high value of accuracy, sensitivity and selectivity (more than 90 %). Finally, value of area under the curve of Receiver Operating Characteristic (AUC-ROC) showed value of 0.81 for Raman range between 800 cm-1 and 1800 cm-1 and 0.86 for 2800 cm-1-3000 cm-1 range. Obtained results clearly showed possibility of Raman spectroscopy in detection of ACS from serum.
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