Machine learning for detection of stenoses and aneurysms: application in a physiologically realistic virtual patient database.
In: Biomechanics and modeling in mechanobiology, Jg. 20 (2021-12-01), Heft 6, S. 2097-2146
Online
academicJournal
Zugriff:
This study presents an application of machine learning (ML) methods for detecting the presence of stenoses and aneurysms in the human arterial system. Four major forms of arterial disease-carotid artery stenosis (CAS), subclavian artery stenosis (SAS), peripheral arterial disease (PAD), and abdominal aortic aneurysms (AAA)-are considered. The ML methods are trained and tested on a physiologically realistic virtual patient database (VPD) containing 28,868 healthy subjects, adapted from the authors previous work and augmented to include disease. It is found that the tree-based methods of Random Forest and Gradient Boosting outperform other approaches. The performance of ML methods is quantified through the [Formula: see text] score and computation of sensitivities and specificities. When using six haemodynamic measurements (pressure in the common carotid, brachial, and radial arteries; and flow-rate in the common carotid, brachial, and femoral arteries), it is found that maximum [Formula: see text] scores larger than 0.9 are achieved for CAS and PAD, larger than 0.85 for SAS, and larger than 0.98 for both low- and high-severity AAAs. Corresponding sensitivities and specificities are larger than 90% for CAS and PAD, larger than 85% for SAS, and larger than 98% for both low- and high-severity AAAs. When reducing the number of measurements, performance is degraded by less than 5% when three measurements are used, and less than 10% when only two measurements are used for classification. For AAA, it is shown that [Formula: see text] scores larger than 0.85 and corresponding sensitivities and specificities larger than 85% are achievable when using only a single measurement. The results are encouraging to pursue AAA monitoring and screening through wearable devices which can reliably measure pressure or flow-rates.
(© 2021. The Author(s).)
Titel: |
Machine learning for detection of stenoses and aneurysms: application in a physiologically realistic virtual patient database.
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Autor/in / Beteiligte Person: | Jones, G ; Parr, J ; Nithiarasu, P ; Pant, S |
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Zeitschrift: | Biomechanics and modeling in mechanobiology, Jg. 20 (2021-12-01), Heft 6, S. 2097-2146 |
Veröffentlichung: | Berlin ; New York : Springer, c2002-, 2021 |
Medientyp: | academicJournal |
ISSN: | 1617-7940 (electronic) |
DOI: | 10.1007/s10237-021-01497-7 |
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