Machine Learning-Directed Electrical Impedance Tomography to Predict Metabolically Vulnerable Plaques


The characterization of atherosclerotic plaques to predict their vulnerability to rupture remains a diagnostic challenge. Despite existing imaging modalities, none have proven their abilities to identify metabolically active oxidized low-density lipoprotein (oxLDL), a marker of plaque vulnerability. To this end, we developed a machine learning-directed electrochemical impedance spectroscopy (EIS) platform to analyze oxLDL-rich plaques, with immunohistology serving as the ground truth. We fabricated the EIS sensor by affixing a six-point microelectrode configuration onto a silicone balloon catheter and electroplating the surface with platinum black (PtB) to improve the charge transfer efficiency at the electrochemical interface. To demonstrate clinical translation, we deployed the EIS sensor to the coronary arteries of an explanted human heart from a patient undergoing heart transplant and interrogated the atherosclerotic lesions to reconstruct the 3D EIS profiles of oxLDL-rich atherosclerotic plaques in both right coronary and left descending coronary arteries. To establish effective generalization of our methods, we repeated the reconstruction and training process on the common carotid arteries of an unembalmed human cadaver specimen. Our findings indicated that our DenseNet model achieves the most reliable predictions for metabolically vulnerable plaque, yielding an accuracy of 92.59% after 100 epochs of training.

Bioengineering & Translational Medicine, e10616
Justin Chen
Justin Chen
Ph.D. Student