Congratulations to Natasha! She published her first research paper about the application of machine learning to characterize atherosclerotic lesions, imaged with CARS microscopy.
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Development of an image classification pipeline for atherosclerotic plaques assessment using supervised machine learning
During atherosclerosis, the narrowing of the arterial lumen is observed through the accumulation of bio compounds and the formation of plaque within artery walls. A non-linear optical imaging modality (NLOM), coherent anti-stokes Raman scattering (CARS) microscopy, can be used to image lipid-rich structures commonly found in atherosclerotic plaques. By matching the lipid’s molecular vibrational frequencies (CH bonds), it is possible to map the accumulation of lipid-rich structures without the need for exogenous labelling and/or processing of the samples. CARS allows for the visualization of the morphological features of plaque. In combination with supervised machine learning, CARS-imaged morphological features can be used to characterize the progression of atherosclerotic plaques.