Precision medicine in human heart modeling Perspectives, challenges, and opportunities

被引:125
作者
Peirlinck, M. [1 ]
Costabal, F. Sahli [2 ]
Yao, J. [3 ]
Guccione, J. M. [4 ]
Tripathy, S. [5 ]
Wang, Y. [6 ]
Ozturk, D. [7 ]
Segars, P. [8 ]
Morrison, T. M. [9 ]
Levine, S. [3 ]
Kuhl, E. [1 ,10 ]
机构
[1] Stanford Univ, Dept Mech Engn, Stanford, CA 94305 USA
[2] Pontificia Univ Catholica Chile, Dept Mech Engn, Santiago, Chile
[3] Dassault Syst Simulia Corp, Johnston, RI USA
[4] Univ Calif San Francisco, San Francisco, CA 94143 USA
[5] Edwards Lifesci, Irvine, CA USA
[6] Thornton Tomasetti Inc, Santa Clara, CA USA
[7] Capvidia, Leuven, Belgium
[8] Duke Univ, Dept Radiol, Carl E Ravin Adv Imaging Labs, Durham, NC 27710 USA
[9] US FDA, Ctr Devices & Radiol Hlth, Silver Spring, MD USA
[10] Stanford Univ, Dept Bioengn, Stanford, CA 94305 USA
关键词
Precision medicine; Electrophysiology; Cardiac mechanics; Fluid dynamics; Finite element simulation; Machine learning; Digital twin;
D O I
10.1007/s10237-021-01421-z
中图分类号
Q6 [生物物理学];
学科分类号
071011 ;
摘要
Precision medicine is a new frontier in healthcare that uses scientific methods to customize medical treatment to the individual genes, anatomy, physiology, and lifestyle of each person. In cardiovascular health, precision medicine has emerged as a promising paradigm to enable cost-effective solutions that improve quality of life and reduce mortality rates. However, the exact role in precision medicine for human heart modeling has not yet been fully explored. Here, we discuss the challenges and opportunities for personalized human heart simulations, from diagnosis to device design, treatment planning, and prognosis. With a view toward personalization, we map out the history of anatomic, physical, and constitutive human heart models throughout the past three decades. We illustrate recent human heart modeling in electrophysiology, cardiac mechanics, and fluid dynamics and highlight clinically relevant applications of these models for drug development, pacing lead failure, heart failure, ventricular assist devices, edge-to-edge repair, and annuloplasty. With a view toward translational medicine, we provide a clinical perspective on virtual imaging trials and a regulatory perspective on medical device innovation. We show that precision medicine in human heart modeling does not necessarily require a fully personalized, high-resolution whole heart model with an entire personalized medical history. Instead, we advocate for creating personalized models out of population-based libraries with geometric, biological, physical, and clinical information by morphing between clinical data and medical histories from cohorts of patients using machine learning. We anticipate that this perspective will shape the path toward introducing human heart simulations into precision medicine with the ultimate goals to facilitate clinical decision making, guide treatment planning, and accelerate device design.
引用
收藏
页码:803 / 831
页数:29
相关论文
共 179 条
[1]   DukeSim: A Realistic, Rapid, and Scanner-Specific Simulation Framework in Computed Tomography [J].
Abadi, Ehsan ;
Harrawood, Brian ;
Sharma, Shobhit ;
Kapadia, Anuj ;
Segars, William P. ;
Samei, Ehsan .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2019, 38 (06) :1457-1465
[2]  
Abbott Press Resease, 2019, ABB REC FDA APPR EXP
[3]   Patient-specific modeling of dyssynchronous heart failure: A case study [J].
Aguado-Sierra, Jazmin ;
Krishnamurthy, Adarsh ;
Villongco, Christopher ;
Chuang, Joyce ;
Howard, Elliot ;
Gonzales, Matthew J. ;
Omens, Jeff ;
Krummen, David E. ;
Narayan, Sanjiv ;
Kerckhoffs, Roy C. P. ;
McCulloch, Andrew D. .
PROGRESS IN BIOPHYSICS & MOLECULAR BIOLOGY, 2011, 107 (01) :147-155
[4]  
Aksenov A, 2006, P AB US C
[5]  
Aksenov A., 1998, IMPACT ANN SUPRA ANN, V377, P79
[6]   Human Heart Blood Flow Numerical Modelling and Simulations [J].
Aksenov, Andrey ;
Zhluktov, Sergey ;
Zietak, Wojciech ;
Cotton, Ross ;
Vucinic, Dean .
ADVANCES IN VISUALIZATION AND OPTIMIZATION TECHNIQUES FOR MULTIDISCIPLINARY RESEARCH: TRENDS IN MODELLING AND SIMULATIONS FOR ENGINEERING APPLICATIONS, 2020, :237-263
[7]  
[Аксёнов Андрей Александрович Aksenov Andrey A.], 2017, [Компьютерные исследования и моделирование, Computer Research and Modeling, Komp'yuternye issledovaniya i modelirovanie], V9, P5, DOI 10.20537/2076-7633-2017-9-5-20
[8]   Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biologica biomedical, and behavioral sciences [J].
Alber, Mark ;
Tepole, Adrian Buganza ;
Cannon, William R. ;
De, Suvranu ;
Dura-Bernal, Salvador ;
Garikipati, Krishna ;
Karniadakis, George ;
Lytton, William W. ;
Perdikaris, Paris ;
Petzold, Linda ;
Kuhl, Ellen .
NPJ DIGITAL MEDICINE, 2019, 2 (1)
[9]   A simple two-variable model of cardiac excitation [J].
Aliev, RR ;
Panfilov, AV .
CHAOS SOLITONS & FRACTALS, 1996, 7 (03) :293-301
[10]   ELECTROMECHANICAL COUPLING IN CARDIAC DYNAMICS: THE ACTIVE STRAIN APPROACH [J].
Ambrosi, D. ;
Arioli, G. ;
Nobile, F. ;
Quarteroni, A. .
SIAM JOURNAL ON APPLIED MATHEMATICS, 2011, 71 (02) :605-621