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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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Bioengineering & Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/btm2.10616 |
| _version_ | 1827388927302959104 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Justin Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T16:27:07Z |
| format | Article |
| id | doaj.art-40484d6d8f84463e9bfc7f0a17bfaa6b |
| institution | Directory Open Access Journal |
| issn | 2380-6761 |
| language | English |
| last_indexed | 2024-03-08T16:27:07Z |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Bioengineering & Translational Medicine |
| spelling | doaj.art-40484d6d8f84463e9bfc7f0a17bfaa6b2024-01-07T04:15:59ZengWileyBioengineering & Translational Medicine2380-67612024-01-0191n/an/a10.1002/btm2.10616Machine learning‐directed electrical impedance tomography to predict metabolically vulnerable plaquesJustin Chen0Shaolei Wang1Kaidong Wang2Parinaz Abiri3Zi‐Yu Huang4Junyi Yin5Alejandro M. Jabalera6Brian Arianpour7Mehrdad Roustaei8Enbo Zhu9Peng Zhao10Susana Cavallero11Sandra Duarte‐Vogel12Elena Stark13Yuan Luo14Peyman Benharash15Yu‐Chong Tai16Qingyu Cui17Tzung K. Hsiai18Department of Bioengineering, Henry Samueli School of Engineering University of California, Los Angeles Los Angeles California USADepartment of Bioengineering, Henry Samueli School of Engineering University of California, Los Angeles Los Angeles California USADivision of Cardiology, Department of Medicine, David Geffen School of Medicine University of California, Los Angeles Los Angeles California USADepartment of Bioengineering, Henry Samueli School of Engineering University of California, Los Angeles Los Angeles California USADepartment of Medical Engineering California Institute of Technology Pasadena California USADepartment of Bioengineering, Henry Samueli School of Engineering University of California, Los Angeles Los Angeles California USADepartment of Bioengineering, Henry Samueli School of Engineering University of California, Los Angeles Los Angeles California USADepartment of Bioengineering, Henry Samueli School of Engineering University of California, Los Angeles Los Angeles California USADepartment of Bioengineering, Henry Samueli School of Engineering University of California, Los Angeles Los Angeles California USADivision of Cardiology, Department of Medicine, David Geffen School of Medicine University of California, Los Angeles Los Angeles California USADivision of Cardiology, Department of Medicine, David Geffen School of Medicine University of California, Los Angeles Los Angeles California USADivision of Cardiology, Department of Medicine, David Geffen School of Medicine University of California, Los Angeles Los Angeles California USADivision of Laboratory Animal Medicine, David Geffen School of Medicine University of California, Los Angeles Los Angeles California USADivision of Anatomy, Department of Pathology and Laboratory Medicine, David Geffen School of Medicine University of California, Los Angeles Los Angeles California USADepartment of Medical Engineering California Institute of Technology Pasadena California USADivision of Cardiothoracic Surgery, Department of Surgery, David Geffen School of Medicine University of California, Los Angeles Los Angeles California USADepartment of Medical Engineering California Institute of Technology Pasadena California USADivision of Cardiology, Department of Medicine, David Geffen School of Medicine University of California, Los Angeles Los Angeles California USADepartment of Bioengineering, Henry Samueli School of Engineering University of California, Los Angeles Los Angeles California USAAbstract 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.https://doi.org/10.1002/btm2.10616atherosclerosiselectrochemical impedance spectroscopymachine learningnanomaterialsoxidized low‐density lipoprotein |
| spellingShingle | 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 Machine learning‐directed electrical impedance tomography to predict metabolically vulnerable plaques Bioengineering & Translational Medicine atherosclerosis electrochemical impedance spectroscopy machine learning nanomaterials oxidized low‐density lipoprotein |
| title | Machine learning‐directed electrical impedance tomography to predict metabolically vulnerable plaques |
| title_full | Machine learning‐directed electrical impedance tomography to predict metabolically vulnerable plaques |
| title_fullStr | Machine learning‐directed electrical impedance tomography to predict metabolically vulnerable plaques |
| title_full_unstemmed | Machine learning‐directed electrical impedance tomography to predict metabolically vulnerable plaques |
| title_short | Machine learning‐directed electrical impedance tomography to predict metabolically vulnerable plaques |
| title_sort | machine learning directed electrical impedance tomography to predict metabolically vulnerable plaques |
| topic | atherosclerosis electrochemical impedance spectroscopy machine learning nanomaterials oxidized low‐density lipoprotein |
| url | https://doi.org/10.1002/btm2.10616 |
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