ArticleBioengineering & translational medicine2024
Machine learning-directed electrical impedance tomography to predict metabolically vulnerable plaques.
Article in Bioengineering & translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 17 citations in OpenAlex.
- Advances in Wearable Bioimaging.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- An intelligent wearable device based on electrical impedance tomography for dynamic and accurate monitoring of voiding function in patients with benign prostatic hyperplasia: protocol for a single-centre self-controlled diagnostic consistency study.Translational andrology and urology · 2026Article
- Harnessing Statistical and Machine Learning Approaches to Analyze Oxidized LDL in Clinical Research.Cell biochemistry and biophysics · 2025Review
- Seeing inside the Body Using Wearable Sensing and Imaging Technologies.Advanced healthcare materials · 2025Review
- Machine learning-assisted point-of-care diagnostics for cardiovascular healthcare.Bioengineering & translational medicine · 2025Review
- Review
- Assessment of Tail-Cutting in Frozen Albacore (Foods (Basel, Switzerland) · 2024Article
- Advances in Machine Learning for Wearable Sensors.ACS nano · 2024Review
- An Unsupervised Learning Tool for Plaque Tissue Characterization in Histopathological Images.Sensors (Basel, Switzerland) · 2024Article
- Non-Invasive Detection of Early-Stage Fatty Liver Disease via an On-Skin Impedance Sensor and Attention-Based Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Electrical Impedance Tomography, Artificial Intelligence, and Variable Ventilation: Transforming Respiratory Monitoring and Treatment in Critical Care.Journal of personalized medicine · 2024Review
- Past, present, and future of electrical impedance tomography and myography for medical applications: a scoping review.Frontiers in bioengineering and biotechnology · 2024Article
- Machine learning-directed electrical impedance tomography to predict metabolically vulnerable plaques.Bioengineering & translational medicine · 2024Article
Corrections and comments
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Authors and funding
19 authors at 2 institutions in 1 country.
Funding
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 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.