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Benjamin MM, Rabbat MG. Artificial Intelligence in Transcatheter Aortic Valve Replacement: Its Current Role and Ongoing Challenges. Diagnostics (Basel) 2024; 14:261. [PMID: 38337777 PMCID: PMC10855497 DOI: 10.3390/diagnostics14030261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 01/18/2024] [Accepted: 01/20/2024] [Indexed: 02/12/2024] Open
Abstract
Transcatheter aortic valve replacement (TAVR) has emerged as a viable alternative to surgical aortic valve replacement, as accumulating clinical evidence has demonstrated its safety and efficacy. TAVR indications have expanded beyond high-risk or inoperable patients to include intermediate and low-risk patients with severe aortic stenosis. Artificial intelligence (AI) is revolutionizing the field of cardiology, aiding in the interpretation of medical imaging and developing risk models for at-risk individuals and those with cardiac disease. This article explores the growing role of AI in TAVR procedures and assesses its potential impact, with particular focus on its ability to improve patient selection, procedural planning, post-implantation monitoring and contribute to optimized patient outcomes. In addition, current challenges and future directions in AI implementation are highlighted.
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Affiliation(s)
- Mina M. Benjamin
- Division of Cardiovascular Medicine, SSM—Saint Louis University Hospital, Saint Louis University, Saint Louis, MO 63104, USA
| | - Mark G. Rabbat
- Department of Cardiovascular Medicine, Loyola University Medical Center, Maywood, IL 60153, USA;
- Department of Cardiology, Edward Hines Jr. VA Hospital, Hines, IL 60141, USA
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2
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Danilov VV, Klyshnikov KY, Onishenko PS, Proutski A, Gankin Y, Melgani F, Ovcharenko EA. Perfect prosthetic heart valve: generative design with machine learning, modeling, and optimization. Front Bioeng Biotechnol 2023; 11:1238130. [PMID: 37781537 PMCID: PMC10541217 DOI: 10.3389/fbioe.2023.1238130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Accepted: 08/22/2023] [Indexed: 10/03/2023] Open
Abstract
Majority of modern techniques for creating and optimizing the geometry of medical devices are based on a combination of computer-aided designs and the utility of the finite element method This approach, however, is limited by the number of geometries that can be investigated and by the time required for design optimization. To address this issue, we propose a generative design approach that combines machine learning (ML) methods and optimization algorithms. We evaluate eight different machine learning methods, including decision tree-based and boosting algorithms, neural networks, and ensembles. For optimal design, we investigate six state-of-the-art optimization algorithms, including Random Search, Tree-structured Parzen Estimator, CMA-ES-based algorithm, Nondominated Sorting Genetic Algorithm, Multiobjective Tree-structured Parzen Estimator, and Quasi-Monte Carlo Algorithm. In our study, we apply the proposed approach to study the generative design of a prosthetic heart valve (PHV). The design constraints of the prosthetic heart valve, including spatial requirements, materials, and manufacturing methods, are used as inputs, and the proposed approach produces a final design and a corresponding score to determine if the design is effective. Extensive testing leads to the conclusion that utilizing a combination of ensemble methods in conjunction with a Tree-structured Parzen Estimator or a Nondominated Sorting Genetic Algorithm is the most effective method in generating new designs with a relatively low error rate. Specifically, the Mean Absolute Percentage Error was found to be 11.8% and 10.2% for lumen and peak stress prediction respectively. Furthermore, it was observed that both optimization techniques result in design scores of approximately 95%. From both a scientific and applied perspective, this approach aims to select the most efficient geometry with given input parameters, which can then be prototyped and used for subsequent in vitro experiments. By proposing this approach, we believe it will replace or complement CAD-FEM-based modeling, thereby accelerating the design process and finding better designs within given constraints. The repository, which contains the essential components of the study, including curated source code, dataset, and trained models, is publicly available at https://github.com/ViacheslavDanilov/generative_design.
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Affiliation(s)
| | - Kirill Y. Klyshnikov
- Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
| | - Pavel S. Onishenko
- Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
| | | | | | | | - Evgeny A. Ovcharenko
- Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
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3
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Tahir AM, Mutlu O, Bensaali F, Ward R, Ghareeb AN, Helmy SMHA, Othman KT, Al-Hashemi MA, Abujalala S, Chowdhury MEH, Alnabti ARDMH, Yalcin HC. Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes. J Clin Med 2023; 12:4774. [PMID: 37510889 PMCID: PMC10381346 DOI: 10.3390/jcm12144774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/08/2023] [Accepted: 04/10/2023] [Indexed: 07/30/2023] Open
Abstract
Aortic valve defects are among the most prevalent clinical conditions. A severely damaged or non-functioning aortic valve is commonly replaced with a bioprosthetic heart valve (BHV) via the transcatheter aortic valve replacement (TAVR) procedure. Accurate pre-operative planning is crucial for a successful TAVR outcome. Assessment of computational fluid dynamics (CFD), finite element analysis (FEA), and fluid-solid interaction (FSI) analysis offer a solution that has been increasingly utilized to evaluate BHV mechanics and dynamics. However, the high computational costs and the complex operation of computational modeling hinder its application. Recent advancements in the deep learning (DL) domain can offer a real-time surrogate that can render hemodynamic parameters in a few seconds, thus guiding clinicians to select the optimal treatment option. Herein, we provide a comprehensive review of classical computational modeling approaches, medical imaging, and DL approaches for planning and outcome assessment of TAVR. Particularly, we focus on DL approaches in previous studies, highlighting the utilized datasets, deployed DL models, and achieved results. We emphasize the critical challenges and recommend several future directions for innovative researchers to tackle. Finally, an end-to-end smart DL framework is outlined for real-time assessment and recommendation of the best BHV design for TAVR. Ultimately, deploying such a framework in future studies will support clinicians in minimizing risks during TAVR therapy planning and will help in improving patient care.
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Affiliation(s)
- Anas M Tahir
- Electrical and Computer Engineering Department, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Biomedical Research Center, Qatar University, Doha 2713, Qatar
| | - Onur Mutlu
- Biomedical Research Center, Qatar University, Doha 2713, Qatar
| | - Faycal Bensaali
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Rabab Ward
- Electrical and Computer Engineering Department, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Abdel Naser Ghareeb
- Heart Hospital, Hamad Medical Corporation, Doha 3050, Qatar
- Faculty of Medicine, Al Azhar University, Cairo 11884, Egypt
| | - Sherif M H A Helmy
- Noninvasive Cardiology Section, Cardiology Department, Heart Hospital, Hamad Medical Corporation, Doha 3050, Qatar
| | | | - Mohammed A Al-Hashemi
- Noninvasive Cardiology Section, Cardiology Department, Heart Hospital, Hamad Medical Corporation, Doha 3050, Qatar
| | | | | | | | - Huseyin C Yalcin
- Biomedical Research Center, Qatar University, Doha 2713, Qatar
- Department of Biomedical Science, College of Health Sciences, QU Health, Qatar University, Doha 2713, Qatar
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4
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Waite JR, Tan SY, Saha H, Sarkar S, Sarkar A. Few-shot deep learning for AFM force curve characterization of single-molecule interactions. PATTERNS (NEW YORK, N.Y.) 2023; 4:100672. [PMID: 36699737 PMCID: PMC9868661 DOI: 10.1016/j.patter.2022.100672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/29/2022] [Accepted: 12/07/2022] [Indexed: 01/09/2023]
Abstract
Deep learning (DL)-based analytics has the scope to transform the field of atomic force microscopy (AFM) with regard to fast and bias-free measurement characterization. For example, AFM force-distance curves can help estimate important parameters of binding kinetics, such as the most probable rupture force, binding probability, association, and dissociation constants, as well as receptor density on live cells. Other than the ideal single-rupture event in the force-distance curves, there can be no-rupture, double-rupture, or multiple-rupture events. The current practice is to go through such datasets manually, which can be extremely tedious work for the experimentalists. We address this issue by adopting a few-shot learning approach to build sample-efficient DL models that demonstrate better performance than shallow ML models while matching the performance of moderately trained humans. We also release our AFM force curve dataset and annotations publicly as a benchmark for the research community.
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Affiliation(s)
- Joshua R. Waite
- Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA
| | - Sin Yong Tan
- Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA
| | - Homagni Saha
- Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA
| | - Soumik Sarkar
- Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA
| | - Anwesha Sarkar
- Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA,Corresponding author
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5
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Busnatu ȘS, Niculescu AG, Bolocan A, Andronic O, Pantea Stoian AM, Scafa-Udriște A, Stănescu AMA, Păduraru DN, Nicolescu MI, Grumezescu AM, Jinga V. A Review of Digital Health and Biotelemetry: Modern Approaches towards Personalized Medicine and Remote Health Assessment. J Pers Med 2022; 12:jpm12101656. [PMID: 36294795 PMCID: PMC9604784 DOI: 10.3390/jpm12101656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 09/28/2022] [Accepted: 09/30/2022] [Indexed: 11/05/2022] Open
Abstract
With the prevalence of digitalization in all aspects of modern society, health assessment is becoming digital too. Taking advantage of the most recent technological advances and approaching medicine from an interdisciplinary perspective has allowed for important progress in healthcare services. Digital health technologies and biotelemetry devices have been more extensively employed for preventing, detecting, diagnosing, monitoring, and predicting the evolution of various diseases, without requiring wires, invasive procedures, or face-to-face interaction with medical personnel. This paper aims to review the concepts correlated to digital health, classify and describe biotelemetry devices, and present the potential of digitalization for remote health assessment, the transition to personalized medicine, and the streamlining of clinical trials.
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Affiliation(s)
- Ștefan Sebastian Busnatu
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | - Adelina-Gabriela Niculescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Politehnica University of Bucharest, 011061 Bucharest, Romania
| | - Alexandra Bolocan
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | - Octavian Andronic
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | | | - Alexandru Scafa-Udriște
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | | | - Dan Nicolae Păduraru
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | - Mihnea Ioan Nicolescu
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | - Alexandru Mihai Grumezescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Politehnica University of Bucharest, 011061 Bucharest, Romania
- Research Institute of the University of Bucharest—ICUB, University of Bucharest, 050657 Bucharest, Romania
- Academy of Romanian Scientists, Ilfov No. 3, 050044 Bucharest, Romania
- Correspondence:
| | - Viorel Jinga
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
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6
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Busnatu Ș, Niculescu AG, Bolocan A, Petrescu GED, Păduraru DN, Năstasă I, Lupușoru M, Geantă M, Andronic O, Grumezescu AM, Martins H. Clinical Applications of Artificial Intelligence-An Updated Overview. J Clin Med 2022; 11:jcm11082265. [PMID: 35456357 PMCID: PMC9031863 DOI: 10.3390/jcm11082265] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/09/2022] [Accepted: 04/14/2022] [Indexed: 12/16/2022] Open
Abstract
Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out.
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Affiliation(s)
- Ștefan Busnatu
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Adelina-Gabriela Niculescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, Politehnica University of Bucharest, 011061 Bucharest, Romania;
| | - Alexandra Bolocan
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - George E. D. Petrescu
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Dan Nicolae Păduraru
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Iulian Năstasă
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Mircea Lupușoru
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Marius Geantă
- Centre for Innovation in Medicine, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania;
| | - Octavian Andronic
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Alexandru Mihai Grumezescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, Politehnica University of Bucharest, 011061 Bucharest, Romania;
- Research Institute of the University of Bucharest—ICUB, University of Bucharest, 050657 Bucharest, Romania
- Academy of Romanian Scientists, Ilfov No. 3, 50044 Bucharest, Romania
- Correspondence:
| | - Henrique Martins
- Faculty of Health Sciences, Universidade da Beira Interior, 6200-506 Covilha, Portugal;
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7
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Johnson EL, Laurence DW, Xu F, Crisp CE, Mir A, Burkhart HM, Lee CH, Hsu MC. Parameterization, geometric modeling, and isogeometric analysis of tricuspid valves. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021; 384:113960. [PMID: 34262232 PMCID: PMC8274564 DOI: 10.1016/j.cma.2021.113960] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Approximately 1.6 million patients in the United States are affected by tricuspid valve regurgitation, which occurs when the tricuspid valve does not close properly to prevent backward blood flow into the right atrium. Despite its critical role in proper cardiac function, the tricuspid valve has received limited research attention compared to the mitral and aortic valves on the left side of the heart. As a result, proper valvular function and the pathologies that may cause dysfunction remain poorly understood. To promote further investigations of the biomechanical behavior and response of the tricuspid valve, this work establishes a parameter-based approach that provides a template for tricuspid valve modeling and simulation. The proposed tricuspid valve parameterization presents a comprehensive description of the leaflets and the complex chordae tendineae for capturing the typical three-cusp structural deformation observed from medical data. This simulation framework develops a practical procedure for modeling tricuspid valves and offers a robust, flexible approach to analyze the performance and effectiveness of various valve configurations using isogeometric analysis. The proposed methods also establish a baseline to examine the tricuspid valve's structural deformation, perform future investigations of native valve configurations under healthy and disease conditions, and optimize prosthetic valve designs.
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Affiliation(s)
- Emily L. Johnson
- Department of Mechanical Engineering, Iowa State University, 2043 Black Engineering, Ames, Iowa 50011, USA
| | - Devin W. Laurence
- School of Aerospace and Mechanical Engineering, The University of Oklahoma, Norman, Oklahoma 73019, USA
| | - Fei Xu
- Ansys Inc., 807 Las Cimas Parkway, Austin, Texas 78746, USA
| | - Caroline E. Crisp
- Department of Mechanical Engineering, Iowa State University, 2043 Black Engineering, Ames, Iowa 50011, USA
| | - Arshid Mir
- Division of Pediatric Cardiology, Department of Pediatrics, The University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma 73104, USA
| | - Harold M. Burkhart
- Division of Cardiothoracic Surgery, Department of Surgery, The University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma 73104, USA
| | - Chung-Hao Lee
- School of Aerospace and Mechanical Engineering, The University of Oklahoma, Norman, Oklahoma 73019, USA
- Institute for Biomedical Engineering, Science and Technology (IBEST), The University of Oklahoma, Norman, Oklahoma 73019, USA
| | - Ming-Chen Hsu
- Department of Mechanical Engineering, Iowa State University, 2043 Black Engineering, Ames, Iowa 50011, USA
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8
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Scafa Udriște A, Niculescu AG, Grumezescu AM, Bădilă E. Cardiovascular Stents: A Review of Past, Current, and Emerging Devices. MATERIALS (BASEL, SWITZERLAND) 2021; 14:2498. [PMID: 34065986 PMCID: PMC8151529 DOI: 10.3390/ma14102498] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/10/2021] [Accepted: 05/10/2021] [Indexed: 12/11/2022]
Abstract
One of the leading causes of morbidity and mortality worldwide is coronary artery disease, a condition characterized by the narrowing of the artery due to plaque deposits. The standard of care for treating this disease is the introduction of a stent at the lesion site. This life-saving tubular device ensures vessel support, keeping the blood-flow path open so that the cardiac muscle receives its vital nutrients and oxygen supply. Several generations of stents have been iteratively developed towards improving patient outcomes and diminishing adverse side effects following the implanting procedure. Moving from bare-metal stents to drug-eluting stents, and recently reaching bioresorbable stents, this research field is under continuous development. To keep up with how stent technology has advanced in the past few decades, this paper reviews the evolution of these devices, focusing on how they can be further optimized towards creating an ideal vascular scaffold.
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Affiliation(s)
- Alexandru Scafa Udriște
- Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania; (A.S.U.); (E.B.)
- Cardiology Department, Clinical Emergency Hospital Bucharest, 014461 Bucharest, Romania
| | - Adelina-Gabriela Niculescu
- Faculty of Engineering in Foreign Languages, University Politehnica of Bucharest, 060042 Bucharest, Romania;
| | - Alexandru Mihai Grumezescu
- Faculty of Applied Chemistry and Materials Science, University Politehnica of Bucharest, 060042 Bucharest, Romania
- Research Institute of the University of Bucharest—ICUB, University of Bucharest, 050657 Bucharest, Romania
| | - Elisabeta Bădilă
- Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania; (A.S.U.); (E.B.)
- Internal Medicine Department, Clinical Emergency Hospital Bucharest, 014461 Bucharest, Romania
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9
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Lee XY, Waite JR, Yang CH, Pokuri BSS, Joshi A, Balu A, Hegde C, Ganapathysubramanian B, Sarkar S. Fast inverse design of microstructures via generative invariance networks. NATURE COMPUTATIONAL SCIENCE 2021; 1:229-238. [PMID: 38183201 DOI: 10.1038/s43588-021-00045-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 02/22/2021] [Indexed: 01/07/2024]
Abstract
The problem of the efficient design of material microstructures exhibiting desired properties spans a variety of engineering and science applications. The ability to rapidly generate microstructures that exhibit user-specified property distributions can transform the iterative process of traditional microstructure-sensitive design. We reformulate the microstructure design process using a constrained generative adversarial network (GAN) model. This approach explicitly encodes invariance constraints within GANs to generate two-phase morphologies for photovoltaic applications obeying design specifications: specifically, user-defined short-circuit current density and fill factor combinations. Such invariance constraints can be represented by differentiable, deep learning-based surrogates of full physics models mapping microstructures to photovoltaic properties. Furthermore, we propose a multi-fidelity surrogate that reduces expensive label requirements by a factor of five. Our framework enables the incorporation of expensive or non-differentiable constraints for the fast generation of microstructures (in 190 ms) with user-defined properties. Such proposed physics-aware data-driven methods for inverse design problems can be used to considerably accelerate the field of microstructure-sensitive design.
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Affiliation(s)
- Xian Yeow Lee
- Department of Mechanical Engineering, Iowa State University, Ames, IA, USA
| | - Joshua R Waite
- Department of Mechanical Engineering, Iowa State University, Ames, IA, USA
| | - Chih-Hsuan Yang
- Department of Mechanical Engineering, Iowa State University, Ames, IA, USA
| | | | - Ameya Joshi
- Tandon School of Engineering, New York University, Brooklyn, NY, USA
| | - Aditya Balu
- Department of Mechanical Engineering, Iowa State University, Ames, IA, USA
| | - Chinmay Hegde
- Tandon School of Engineering, New York University, Brooklyn, NY, USA
| | | | - Soumik Sarkar
- Department of Mechanical Engineering, Iowa State University, Ames, IA, USA.
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Gulbulak U, Gecgel O, Ertas A. A deep learning application to approximate the geometric orifice and coaptation areas of the polymeric heart valves under time - varying transvalvular pressure. J Mech Behav Biomed Mater 2021; 117:104371. [PMID: 33610020 DOI: 10.1016/j.jmbbm.2021.104371] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 12/29/2020] [Accepted: 01/26/2021] [Indexed: 11/20/2022]
Abstract
Machine learning and deep learning frameworks have been presented as a substitute for lengthy computational analysis, such as finite element analysis, computational fluid dynamics, and fluid-structure interaction. In this study, our objective was to apply a deep learning framework to predict the geometric orifice (GOA) and the coaptation areas (CA) of the polymeric heart valves under the time-varying transvalvular pressure. 377 different valve geometries were generated by changing the control coordinates of the attachment and the belly curve. The GOA and the CA values were obtained at the maximum and the minimum transvalvular pressure, respectively. The results showed that the applied framework can accurately predict the GOA and the CA despite being trained with a relatively smaller data set. The presented framework can reduce the required time of the lengthy FE frameworks.
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Affiliation(s)
- Utku Gulbulak
- Department of Mechanical Engineering, Texas Tech University, Lubbock, TX, 79409, USA.
| | - Ozhan Gecgel
- Department of Mechanical Engineering, Texas Tech University, Lubbock, TX, 79409, USA
| | - Atila Ertas
- Department of Mechanical Engineering, Texas Tech University, Lubbock, TX, 79409, USA
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11
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The effect of fundamental curves on geometric orifice and coaptation areas of polymeric heart valves. J Mech Behav Biomed Mater 2020; 112:104039. [DOI: 10.1016/j.jmbbm.2020.104039] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 08/04/2020] [Accepted: 08/12/2020] [Indexed: 12/29/2022]
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12
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Intelligent Calibration of Static FEA Computations Based on Terrestrial Laser Scanning Reference. SENSORS 2020; 20:s20226439. [PMID: 33187250 PMCID: PMC7696380 DOI: 10.3390/s20226439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 11/04/2020] [Accepted: 11/09/2020] [Indexed: 11/17/2022]
Abstract
The demand for efficient and accurate finite element analysis (FEA) is becoming more prevalent with the increase in advanced calibration technologies and sensor-based monitoring methods. The current research explores a deep learning-based methodology to calibrate FEA results. The utilization of monitoring reference results from measurements, e.g., terrestrial laser scanning, can help to capture the actual features in the static loading process. We learn the deviation sequence results between the standard FEA computations with the simplified geometry and refined reference values by the long short-term memory method. The complex changing principles in different deviations are trained and captured effectively in the training process of deep learning. Hence, we generate the FEA sequence results corresponding to next adjacent loading steps. The final FEA computations are calibrated by the threshold control. The calibration reduces the mean square errors of the FEA future sequence results significantly. This strengthens the calibration depth. Consequently, the calibration of FEA computations with deep learning can play a helpful role in the prediction and monitoring problems regarding the future structural behaviors.
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