Fractional-order Windkessel model is proposed to describe the aortic input impedance. Compared with the conventional arterial Windkessel, the main advantage of the proposed model is the consideration of the viscoelastic nature of the arterial wall using the fractional-order capacitor (FOC). The proposed model, along with the standard two-element Windkessel, three-element Windkessel, and the viscoelastic Windkessel models, are assessed and compared using in-silico data. The results show that the fractional-order model fits better the moduli of the aortic input impedance and fairly approximates the phase angle. In addition, by its very nature, the pseudo-capacitance of FOC makes the proposed model's dynamic compliance complex and frequency-dependent. The analysis of the proposed fractional-order model indicates that fractional-order impedance yields a powerful tool for a flexible characterization of the arterial hemodynamics.
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http://dx.doi.org/10.1109/OJEMB.2020.2988179 | DOI Listing |
Eur Heart J Digit Health
January 2025
Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Aims: Aortic stenosis (AS) is a common and progressive disease, which, if left untreated, results in increased morbidity and mortality. Monitoring and follow-up care can be challenging due to significant variability in disease progression. This study aimed to develop machine learning models to predict the risks of disease progression and mortality in patients with mild AS.
View Article and Find Full Text PDFQuant Imaging Med Surg
January 2025
Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Background: Due to the low contrast between the vascular lumen and vessel wall, conventional computed tomography (CT) is not an effective method for visualizing the vessel wall. The purpose of this study was to assess the feasibility of vessel wall visualization using contrast-enhanced dual-energy CT (DECT)-derived water-calcium material decomposition (WMD) and subtraction-based dark-blood imaging (DBI). An additional objective of this study was to determine the association of descending aorta wall thickness (WT) and wall area (WA) with cardiovascular disease (CVD) risk factors and to ascertain the potential of DECT-derived WT and WA as image markers for identifying individuals at high risk for future CVD.
View Article and Find Full Text PDFAm J Cardiol
January 2025
Department of Internal Medicine, Cleveland Clinic Foundation, Cleveland, OH, USA. Electronic address:
Acute aortic dissection (AD) is a critical condition characterized by high mortality and frequent misdiagnoses, primarily due to symptom overlap with other medical pathologies. This study explores the diagnostic utility of ChatGPT 4.0, an artificial intelligence model developed by OpenAI, in identifying acute AD from patients' presentations and general physical examination findings documented in published case reports.
View Article and Find Full Text PDFAnn Thorac Surg Short Rep
June 2024
Banner University Medical Center, Tucson, Arizona.
Mechanical valve leaflets have the potential to detach and migrate to unintended locations, leading to life- and limb-threatening situations. We report a unique case of a dislodged mechanical aortic valve leaflet in the right iliac artery bifurcation after a redo mitral valve replacement. This was promptly recognized by input from a multidisciplinary team, allowing immediate correction of the aortic valve insufficiency followed by staged retrieval of the dislodged leaflet to avoid vascular complications.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.
Subject-specific parameters in lumped hemodynamic models of the cardiovascular system can be estimated using data from experimental measurements, but the parameter estimation may be hampered by the variability in the input data. In this study, we investigate the influence of inter-sequence, intra-observer, and inter-observer variability in input parameters on estimation of subject-specific model parameters using a previously developed approach for model-based analysis of data from 4D Flow MRI acquisitions and cuff pressure measurements. The investigated parameters describe left ventricular time-varying elastance and aortic compliance.
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