A Deep Learning Approach to Using Wearable Seismocardiography (SCG) for Diagnosing Aortic Valve Stenosis and Predicting Aortic Hemodynamics Obtained by 4D Flow MRI.
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| Title: | A Deep Learning Approach to Using Wearable Seismocardiography (SCG) for Diagnosing Aortic Valve Stenosis and Predicting Aortic Hemodynamics Obtained by 4D Flow MRI. |
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| Authors: | Ebrahimkhani, Mahmoud1 (AUTHOR), Johnson, Ethan M. I.1 (AUTHOR), Sodhi, Aparna2 (AUTHOR), Robinson, Joshua D.1,2,3 (AUTHOR), Rigsby, Cynthia K.1,2,3 (AUTHOR), Allen, Bradly D.1 (AUTHOR), Markl, Michael1,4 (AUTHOR) mmarkl@northwestern.edu |
| Source: | Annals of Biomedical Engineering. Dec2023, Vol. 51 Issue 12, p2802-2811. 10p. |
| Subjects: | Aortic stenosis, Deep learning, Aortic valve, Aortic valve diseases, Heart valve diseases, Receiver operating characteristic curves, Pulsatile flow |
| Abstract: | In this paper, we explored the use of deep learning for the prediction of aortic flow metrics obtained using 4-dimensional (4D) flow magnetic resonance imaging (MRI) using wearable seismocardiography (SCG) devices. 4D flow MRI provides a comprehensive assessment of cardiovascular hemodynamics, but it is costly and time-consuming. We hypothesized that deep learning could be used to identify pathological changes in blood flow, such as elevated peak systolic velocity ( V max ) in patients with heart valve diseases, from SCG signals. We also investigated the ability of this deep learning technique to differentiate between patients diagnosed with aortic valve stenosis (AS), non-AS patients with a bicuspid aortic valve (BAV), non-AS patients with a mechanical aortic valve (MAV), and healthy subjects with a normal tricuspid aortic valve (TAV). In a study of 77 subjects who underwent same-day 4D flow MRI and SCG, we found that the V max values obtained using deep learning and SCGs were in good agreement with those obtained by 4D flow MRI. Additionally, subjects with non-AS TAV, non-AS BAV, non-AS MAV, and AS could be classified with ROC-AUC (area under the receiver operating characteristic curves) values of 92%, 95%, 81%, and 83%, respectively. This suggests that SCG obtained using low-cost wearable electronics may be used as a supplement to 4D flow MRI exams or as a screening tool for aortic valve disease. [ABSTRACT FROM AUTHOR] |
| Copyright of Annals of Biomedical Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 173493338 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Deep Learning Approach to Using Wearable Seismocardiography (SCG) for Diagnosing Aortic Valve Stenosis and Predicting Aortic Hemodynamics Obtained by 4D Flow MRI. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ebrahimkhani%2C+Mahmoud%22">Ebrahimkhani, Mahmoud</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Johnson%2C+Ethan+M%2E+I%2E%22">Johnson, Ethan M. I.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sodhi%2C+Aparna%22">Sodhi, Aparna</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Robinson%2C+Joshua+D%2E%22">Robinson, Joshua D.</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rigsby%2C+Cynthia+K%2E%22">Rigsby, Cynthia K.</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Allen%2C+Bradly+D%2E%22">Allen, Bradly D.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Markl%2C+Michael%22">Markl, Michael</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<i> mmarkl@northwestern.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Annals+of+Biomedical+Engineering%22">Annals of Biomedical Engineering</searchLink>. Dec2023, Vol. 51 Issue 12, p2802-2811. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Aortic+stenosis%22">Aortic stenosis</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Aortic+valve%22">Aortic valve</searchLink><br /><searchLink fieldCode="DE" term="%22Aortic+valve+diseases%22">Aortic valve diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Heart+valve+diseases%22">Heart valve diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Receiver+operating+characteristic+curves%22">Receiver operating characteristic curves</searchLink><br /><searchLink fieldCode="DE" term="%22Pulsatile+flow%22">Pulsatile flow</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this paper, we explored the use of deep learning for the prediction of aortic flow metrics obtained using 4-dimensional (4D) flow magnetic resonance imaging (MRI) using wearable seismocardiography (SCG) devices. 4D flow MRI provides a comprehensive assessment of cardiovascular hemodynamics, but it is costly and time-consuming. We hypothesized that deep learning could be used to identify pathological changes in blood flow, such as elevated peak systolic velocity ( V max ) in patients with heart valve diseases, from SCG signals. We also investigated the ability of this deep learning technique to differentiate between patients diagnosed with aortic valve stenosis (AS), non-AS patients with a bicuspid aortic valve (BAV), non-AS patients with a mechanical aortic valve (MAV), and healthy subjects with a normal tricuspid aortic valve (TAV). In a study of 77 subjects who underwent same-day 4D flow MRI and SCG, we found that the V max values obtained using deep learning and SCGs were in good agreement with those obtained by 4D flow MRI. Additionally, subjects with non-AS TAV, non-AS BAV, non-AS MAV, and AS could be classified with ROC-AUC (area under the receiver operating characteristic curves) values of 92%, 95%, 81%, and 83%, respectively. This suggests that SCG obtained using low-cost wearable electronics may be used as a supplement to 4D flow MRI exams or as a screening tool for aortic valve disease. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Annals of Biomedical Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10439-023-03342-7 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 2802 Subjects: – SubjectFull: Aortic stenosis Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Aortic valve Type: general – SubjectFull: Aortic valve diseases Type: general – SubjectFull: Heart valve diseases Type: general – SubjectFull: Receiver operating characteristic curves Type: general – SubjectFull: Pulsatile flow Type: general Titles: – TitleFull: A Deep Learning Approach to Using Wearable Seismocardiography (SCG) for Diagnosing Aortic Valve Stenosis and Predicting Aortic Hemodynamics Obtained by 4D Flow MRI. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ebrahimkhani, Mahmoud – PersonEntity: Name: NameFull: Johnson, Ethan M. I. – PersonEntity: Name: NameFull: Sodhi, Aparna – PersonEntity: Name: NameFull: Robinson, Joshua D. – PersonEntity: Name: NameFull: Rigsby, Cynthia K. – PersonEntity: Name: NameFull: Allen, Bradly D. – PersonEntity: Name: NameFull: Markl, Michael IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 00906964 Numbering: – Type: volume Value: 51 – Type: issue Value: 12 Titles: – TitleFull: Annals of Biomedical Engineering Type: main |
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