Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond

Arzani, Amirhossein; Wang, Jianxun; Sacks, Michael S.; Shadden, Shawn C.

doi:10.1007/s10439-022-02967-4

Cited by 52 publications

(16 citation statements)

References 127 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The two distinct methodologies are being increasingly used together, in complementary ways, to model and solve biomedical problems, e.g. (1)(2)(3)(4), but with no clear hybridization unfolding yet. We believe they can be combined more deeply within existing modeling theory, with perhaps new applied mathematical theory as well.…”

Section: Epiloguementioning

confidence: 99%

Editorial: Mechanistic, machine learning and hybrid models of the “other” endocrine regulatory systems in health & disease

2023

View full text Add to dashboard Cite

Editorial on the Research TopicMechanistic, machine learning and hybrid models of the 'other' endocrine regulatory systems in health and diseaseWe made this our focus because mathematical modeling of endocrine systems is dominated by models of glucose regulation and dysregulation, the endocrine pancreas, liver, fat and their associated metabolic subsystems. We solicited works on modeling of thyroid, parathyroid, adrenal, gonadal, pituitary and hypothalamus, as well as cytokine regulation of immunomodulating cell-signaling molecules involved in autocrine, paracrine and endocrine signaling.We succeeded in attracting articles from four areas, seven articles utilizing physiologically-based mechanistic (MEC) and one machine learning (ML) modeling.MEC modeling is based primarily on mechanistic information (biochemical, biophysical interconnectivity and dynamical couplings), derived from first-principles, and numerical input-output data for quantification. Machine learning (ML) models are based primarily on input-output and features data, typically in much larger quantities, modeled in a different but complementary way, usually using high-level optimization and statistical modeling techniques. Mathematical optimization and simulation methods were major tools of several of the articles, attesting to the importance of engineering and computational methodologies for quantifying and predicting biological and clinical phenomena.Contributions of the 8 published papers are summarized below. We amplify their main theses, pointing out their novelty and pertinence of their methods and applications within the framework of our Topic.The paper by Hoermann et al. addresses endocrine regulation teleologically, using a mechanistic model of thyroid hormone (TH) regulation to demonstrate their thesis.

show abstract

Section: Epiloguementioning

confidence: 99%

Editorial: Mechanistic, machine learning and hybrid models of the “other” endocrine regulatory systems in health & disease

2023

View full text Add to dashboard Cite

show abstract

“…Considering the significant time requirement of computational modelling, future applications of computational flow analyses in a large patient cohort will require automation and optimisation of the workflow and computational procedures. To this end, efforts should be made by taking advantage of artificial intelligence and machine learning techniques [ 89 ].…”

Section: Cfd Analysis Of Aortic Arch Repairmentioning

confidence: 99%

Evaluating the Haemodynamic Performance of Endografts for Complex Aortic Arch Repair

et al. 2022

View full text Add to dashboard Cite

Thoracic endovascular aortic repair (TEVAR) of aortic aneurysms and dissections involving the arch has evolved over the last two decades. Compared to conventional surgical methods, endovascular repair offers a less invasive treatment option with lower risk and faster recovery. Endografts used in TEVAR vary in design depending on the procedure and application. Novel endografts (e.g., branched stent-graft) were developed to ensure perfusion of blood to the supra-aortic vessels, but their haemodynamic performance and long-term durability have not been adequately studied. This review focuses on the use of computational modelling to study haemodynamics in commercially available endografts designed for complex aortic arch repair. First, we summarise the currently adopted workflow for computational fluid dynamics (CFD) modelling, including geometry reconstruction, boundary conditions, flow models, and haemodynamic metrics of interest. This is followed by a review of recently (2010-present) published CFD studies on complex aortic arch repair, using both idealized and patient-specific models. Finally, we introduce some of the promising techniques that can be potentially applied to predict post-operative outcomes.

show abstract

“…Machine learning and deep learning algorithms have been used to accelerate the diagnosis and prediction of cardiovascular diseases [ 27 , 29 , 30 , 31 ]. For more detailed examples of the performance of ML and DL approaches for cardiovascular applications, we refer to Section 3 .…”

Section: Overview Of Artificial Intelligencementioning

confidence: 99%

Interplay between Artificial Intelligence and Biomechanics Modeling in the Cardiovascular Disease Prediction

Liu

Deng

et al. 2022

Biomedicines

View full text Add to dashboard Cite

Cardiovascular disease (CVD) is the most common cause of morbidity and mortality worldwide, and early accurate diagnosis is the key point for improving and optimizing the prognosis of CVD. Recent progress in artificial intelligence (AI), especially machine learning (ML) technology, makes it possible to predict CVD. In this review, we first briefly introduced the overview development of artificial intelligence. Then we summarized some ML applications in cardiovascular diseases, including ML−based models to directly predict CVD based on risk factors or medical imaging findings and the ML−based hemodynamics with vascular geometries, equations, and methods for indirect assessment of CVD. We also discussed case studies where ML could be used as the surrogate for computational fluid dynamics in data−driven models and physics−driven models. ML models could be a surrogate for computational fluid dynamics, accelerate the process of disease prediction, and reduce manual intervention. Lastly, we briefly summarized the research difficulties and prospected the future development of AI technology in cardiovascular diseases.

show abstract

Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond

Cited by 52 publications

References 127 publications

Editorial: Mechanistic, machine learning and hybrid models of the “other” endocrine regulatory systems in health & disease

Editorial: Mechanistic, machine learning and hybrid models of the “other” endocrine regulatory systems in health & disease

Evaluating the Haemodynamic Performance of Endografts for Complex Aortic Arch Repair

Interplay between Artificial Intelligence and Biomechanics Modeling in the Cardiovascular Disease Prediction

Contact Info

Product

Resources

About