Machine learning‐directed electrical impedance tomography to predict metabolically vulnerable plaques

• Published in: Bioengineering & translational medicine Vol. 9; no. 1; pp. e10616 - n/a
• Main Authors: Chen, Justin, Wang, Shaolei, Wang, Kaidong, Abiri, Parinaz, Huang, Zi‐Yu, Yin, Junyi, Jabalera, Alejandro M., Arianpour, Brian, Roustaei, Mehrdad, Zhu, Enbo, Zhao, Peng, Cavallero, Susana, Duarte‐Vogel, Sandra, Stark, Elena, Luo, Yuan, Benharash, Peyman, Tai, Yu‐Chong, Cui, Qingyu, Hsiai, Tzung K.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.01.2024; Wiley
• Subjects: Arteries; Atherosclerosis; Biomarkers; Cadavers; Calcification; Cardiovascular disease; Carotid arteries; Charge efficiency; Charge transfer; Coronary vessels; Datasets; Electrical impedance; Electrochemical impedance spectroscopy; Electroplating; Hypotheses; Image reconstruction; Lipids; Machine learning; Microelectrodes; nanomaterials; oxidized low‐density lipoprotein; Platinum black; Spectrum analysis; Tomography; Veins & arteries; atherosclerosis; nanomaterials; electrochemical impedance spectroscopy; oxidized low‐density lipoprotein; machine learning

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....