Aortic Coarctation in the Adult

Love, Barry A.; Fischer, Gregory W.; Stelzer, Paul; Fuster, Valentín

doi:10.1007/978-1-4471-4619-3_184

Pediatric and Congenital Cardiology, Cardiac Surgery and Intensive Care 2013

DOI: 10.1007/978-1-4471-4619-3_184

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Aortic Coarctation in the Adult

Barry A. Love¹,

Gregory W. Fischer²,

Paul Stelzer³

et al.

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Cited by 2 publications

(1 citation statement)

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“…CoA is one of the most prevalent congenital heart defects, affecting approximately 1,600 births each year in the U.S. 17 . CoA is characterized by a stenosis in the region distally adjacent to the left subclavian artery, creating a pressure gradient (ΔP) that can lead to cardiac failure, aortic and cerebral aneurysms, stroke, premature coronary artery disease, and hypertension 18 . A higher ΔP indicates increased patient risk and is used as a primary diagnostic metric to guide decisions regarding surgical intervention 19 .…”

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confidence: 99%

Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks

Feiger

Gounley

Adler

et al. 2020

Sci Rep

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Comorbidities such as anemia or hypertension and physiological factors related to exertion can influence a patient’s hemodynamics and increase the severity of many cardiovascular diseases. Observing and quantifying associations between these factors and hemodynamics can be difficult due to the multitude of co-existing conditions and blood flow parameters in real patient data. Machine learning-driven, physics-based simulations provide a means to understand how potentially correlated conditions may affect a particular patient. Here, we use a combination of machine learning and massively parallel computing to predict the effects of physiological factors on hemodynamics in patients with coarctation of the aorta. We first validated blood flow simulations against in vitro measurements in 3D-printed phantoms representing the patient’s vasculature. We then investigated the effects of varying the degree of stenosis, blood flow rate, and viscosity on two diagnostic metrics – pressure gradient across the stenosis (ΔP) and wall shear stress (WSS) - by performing the largest simulation study to date of coarctation of the aorta (over 70 million compute hours). Using machine learning models trained on data from the simulations and validated on two independent datasets, we developed a framework to identify the minimal training set required to build a predictive model on a per-patient basis. We then used this model to accurately predict ΔP (mean absolute error within 1.18 mmHg) and WSS (mean absolute error within 0.99 Pa) for patients with this disease.

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mentioning

confidence: 99%

Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks

Feiger

Gounley

Adler

et al. 2020

Sci Rep

View full text Add to dashboard Cite

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Deep Learning for Computational Hemodynamics: A Brief Review of Recent Advances

Taebi

2022

Fluids

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Computational fluid dynamics (CFD) modeling of blood flow plays an important role in better understanding various medical conditions, designing more effective drug delivery systems, and developing novel diagnostic methods and treatments. However, despite significant advances in computational technology and resources, the expensive computational cost of these simulations still hinders their transformation from a research interest to a clinical tool. This bottleneck is even more severe for image-based, patient-specific CFD simulations with realistic boundary conditions and complex computational domains, which make such simulations excessively expensive. To address this issue, deep learning approaches have been recently explored to accelerate computational hemodynamics simulations. In this study, we review recent efforts to integrate deep learning with CFD and discuss the applications of this approach in solving hemodynamics problems, such as blood flow behavior in aorta and cerebral arteries. We also discuss potential future directions in the field. In this review, we suggest that incorporating physiologic understandings and underlying fluid mechanics laws in deep learning models will soon lead to a paradigm shift in the development novel non-invasive computational medical decisions.

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Aortic Coarctation in the Adult

Cited by 2 publications

References 46 publications

Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks

Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks

Deep Learning for Computational Hemodynamics: A Brief Review of Recent Advances

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