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

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

Abstract 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 vulne...

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Bibliographic Details
Main Authors: Justin Chen, Shaolei Wang, Kaidong Wang, Parinaz Abiri, Zi‐Yu Huang, Junyi Yin, Alejandro M. Jabalera, Brian Arianpour, Mehrdad Roustaei, Enbo Zhu, Peng Zhao, Susana Cavallero, Sandra Duarte‐Vogel, Elena Stark, Yuan Luo, Peyman Benharash, Yu‐Chong Tai, Qingyu Cui, Tzung K. Hsiai
Format: Article
Language:English
Published: Wiley 2024-01-01
Series:Bioengineering & Translational Medicine
Subjects:
atherosclerosis
electrochemical impedance spectroscopy
machine learning
nanomaterials
oxidized low‐density lipoprotein
Online Access:https://doi.org/10.1002/btm2.10616

Internet

https://doi.org/10.1002/btm2.10616

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