{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T06:15:06Z","timestamp":1740118506897,"version":"3.37.3"},"reference-count":84,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:00:00Z","timestamp":1761091200000},"content-version":"am","delay-in-days":355,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"National Heart Lung and Blood Institute","doi-asserted-by":"publisher","award":["R01HL166508","R01HL163977","R01HL160997"],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Artificial Intelligence in Medicine"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1016\/j.artmed.2024.102995","type":"journal-article","created":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T00:18:27Z","timestamp":1728605907000},"page":"102995","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Rapid estimation of left ventricular contractility with a physics-informed neural network inverse modeling approach"],"prefix":"10.1016","volume":"157","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7173-4396","authenticated-orcid":false,"given":"Ehsan","family":"Naghavi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8786-2479","authenticated-orcid":false,"given":"Haifeng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Jenny S.","family":"Choy","sequence":"additional","affiliation":[]},{"given":"Ghassan","family":"Kassab","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2007-339X","authenticated-orcid":false,"given":"Seungik","family":"Baek","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1123-6428","authenticated-orcid":false,"given":"Lik-Chuan","family":"Lee","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.artmed.2024.102995_b1","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1146\/annurev.bioeng.10.061807.160521","article-title":"Patient-specific modeling of cardiovascular mechanics","volume":"11","author":"Taylor","year":"2009","journal-title":"Annu Rev Biomed Eng"},{"key":"10.1016\/j.artmed.2024.102995_b2","doi-asserted-by":"crossref","DOI":"10.1007\/s12265-018-9792-2","article-title":"Patient-specific cardiovascular computational modeling: Diversity of personalization and challenges","volume":"11","author":"Gray","year":"2018","journal-title":"J Cardiovasc Transl Res"},{"issue":"2","key":"10.1016\/j.artmed.2024.102995_b3","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1038\/s41569-018-0104-y","article-title":"Computational models in cardiology","volume":"16","author":"Niederer","year":"2019","journal-title":"Nat Rev Cardiol"},{"issue":"1","key":"10.1016\/j.artmed.2024.102995_b4","doi-asserted-by":"crossref","DOI":"10.1063\/5.0109400","article-title":"Beyond CFD: Emerging methodologies for predictive simulation in cardiovascular health and disease","volume":"4","author":"Schwarz","year":"2023","journal-title":"Biophys Rev"},{"key":"10.1016\/j.artmed.2024.102995_b5","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1186\/1532-429X-12-59","article-title":"The 20 year evolution of dobutamine stress cardiovascular magnetic resonance","volume":"12","author":"Charoenpanichkit","year":"2010","journal-title":"J Cardiovasc Magn Reson"},{"issue":"6","key":"10.1016\/j.artmed.2024.102995_b6","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1016\/j.jbiomech.2005.02.021","article-title":"Multiscale modelling in biofluidynamics: Application to reconstructive paediatric cardiac surgery","volume":"39","author":"Migliavacca","year":"2006","journal-title":"J Biomech"},{"key":"10.1016\/j.artmed.2024.102995_b7","doi-asserted-by":"crossref","first-page":"3195","DOI":"10.1007\/s10439-010-0083-6","article-title":"Patient-specific modeling of blood flow and pressure in human coronary arteries","volume":"38","author":"Kim","year":"2010","journal-title":"Ann Biomed Eng"},{"issue":"2","key":"10.1016\/j.artmed.2024.102995_b8","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1002\/cnm.2598","article-title":"A systematic comparison between 1-D and 3-D hemodynamics in compliant arterial models","volume":"30","author":"Xiao","year":"2014","journal-title":"Int J Numer Methods Biomed Eng"},{"issue":"1","key":"10.1016\/j.artmed.2024.102995_b9","doi-asserted-by":"crossref","DOI":"10.1002\/cnm.2908","article-title":"Estimating the accuracy of a reduced-order model for the calculation of fractional flow reserve (FFR)","volume":"34","author":"Boileau","year":"2018","journal-title":"Int J Numer Methods Biomed Eng"},{"key":"10.1016\/j.artmed.2024.102995_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbiomech.2019.109595","article-title":"Computational hemodynamics in arteries with the one-dimensional augmented fluid-structure interaction system: viscoelastic parameters estimation and comparison with in-vivo data","volume":"100","author":"Bertaglia","year":"2020","journal-title":"J Biomech"},{"key":"10.1016\/j.artmed.2024.102995_b11","article-title":"Multiscale modeling framework of ventricular-arterial bi-directional interactions in the cardiopulmonary circulation","volume":"11","author":"Shavik","year":"2020","journal-title":"Front Phyiol"},{"issue":"11","key":"10.1016\/j.artmed.2024.102995_b12","doi-asserted-by":"crossref","DOI":"10.1002\/cnm.3246","article-title":"Impact of baseline coronary flow and its distribution on fractional flow reserve prediction","volume":"37","author":"M\u00fcller","year":"2021","journal-title":"Int J Numer Methods Biomed Eng"},{"key":"10.1016\/j.artmed.2024.102995_b13","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2020.611149","article-title":"Patient-specific computational analysis of hemodynamics and wall mechanics and their interactions in pulmonary arterial hypertension","volume":"8","author":"Zambrano","year":"2021","journal-title":"Front Bioeng Biotechnol"},{"key":"10.1016\/j.artmed.2024.102995_b14","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s13239-021-00580-5","article-title":"A 1D\u20133D hybrid model of patient-specific coronary hemodynamics","volume":"13","author":"Grande Guti\u00e9rrez","year":"2022","journal-title":"Cardiovasc Eng Technol"},{"key":"10.1016\/j.artmed.2024.102995_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106766","article-title":"Coupled thermal-hemodynamics computational modeling of cryoballoon ablation for pulmonary vein isolation","volume":"157","author":"Patel","year":"2023","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.artmed.2024.102995_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2023.116312","article-title":"A fluid-solid-growth solver for cardiovascular modeling","volume":"417","author":"Schwarz","year":"2023","journal-title":"Comput Methods Appl Mech Engrg"},{"key":"10.1016\/j.artmed.2024.102995_b17","article-title":"Current state-of-the-art and utilities of machine learning for detection, monitoring, growth prediction, rupture risk assessment, and post-surgical management of abdominal aortic aneurysms","volume":"10","author":"Baek","year":"2022","journal-title":"Appl Eng Sci"},{"key":"10.1016\/j.artmed.2024.102995_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2023.116347","article-title":"Synergistic integration of deep neural networks and finite element method with applications of nonlinear large deformation biomechanics","volume":"416","author":"Liang","year":"2023","journal-title":"Comput Methods Appl Mech Engrg"},{"issue":"6","key":"10.1016\/j.artmed.2024.102995_b19","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1007\/s10439-022-02967-4","article-title":"Machine learning for cardiovascular biomechanics modeling: Challenges and beyond","volume":"50","author":"Arzani","year":"2022","journal-title":"Ann Biomed Eng"},{"issue":"10","key":"10.1016\/j.artmed.2024.102995_b20","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1007\/s11517-019-02029-3","article-title":"Accelerating cardiovascular model building with convolutional neural networks","volume":"57","author":"Maher","year":"2019","journal-title":"Med Biol Eng Comput"},{"key":"10.1016\/j.artmed.2024.102995_b21","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/s41746-019-0193-y","article-title":"Integrating machine learning and multiscale modeling\u2014perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences","volume":"2","author":"Alber","year":"2019","journal-title":"Npj Digit Med"},{"key":"10.1016\/j.artmed.2024.102995_b22","doi-asserted-by":"crossref","DOI":"10.3389\/fphy.2019.00117","article-title":"Prediction of left ventricular mechanics using machine learning","volume":"7","author":"Dabiri","year":"2019","journal-title":"Front Phys"},{"key":"10.1016\/j.artmed.2024.102995_b23","doi-asserted-by":"crossref","first-page":"22298","DOI":"10.1038\/s41598-020-79191-4","article-title":"Application of feed forward and recurrent neural networks in simulation of left ventricular mechanics","volume":"10","author":"Dabiri","year":"2020","journal-title":"Sci Rep"},{"key":"10.1016\/j.artmed.2024.102995_b24","doi-asserted-by":"crossref","first-page":"21980","DOI":"10.1109\/ACCESS.2020.2970143","article-title":"Digital twin: Values, challenges and enablers from a modeling perspective","volume":"8","author":"Rasheed","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.artmed.2024.102995_b25","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1007\/s11831-020-09405-5","article-title":"Multiscale modeling meets machine learning: What can we learn?","volume":"28","author":"Peng","year":"2021","journal-title":"Arch Comput Methods Eng"},{"key":"10.1016\/j.artmed.2024.102995_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102222","article-title":"A deep-learning approach for direct whole-heart mesh reconstruction","volume":"74","author":"Kong","year":"2021","journal-title":"Med Image Anal"},{"key":"10.1016\/j.artmed.2024.102995_b27","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","article-title":"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations","volume":"378","author":"Raissi","year":"2019","journal-title":"J Comput Phys"},{"key":"10.1016\/j.artmed.2024.102995_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2019.112623","article-title":"Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks","volume":"358","author":"Kissas","year":"2020","journal-title":"Comput Methods Appl Mech Engrg"},{"issue":"6481","key":"10.1016\/j.artmed.2024.102995_b29","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1126\/science.aaw4741","article-title":"Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations","volume":"367","author":"Raissi","year":"2020","journal-title":"Science"},{"key":"10.1016\/j.artmed.2024.102995_b30","doi-asserted-by":"crossref","DOI":"10.3389\/fphy.2020.00042","article-title":"Physics-informed neural networks for cardiac activation mapping","volume":"8","author":"Sahli Costabal","year":"2020","journal-title":"Front Phys"},{"key":"10.1016\/j.artmed.2024.102995_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2020.113603","article-title":"Non-invasive inference of thrombus material properties with physics-informed neural networks","volume":"375","author":"Yin","year":"2021","journal-title":"Comput Methods Appl Mech Engrg"},{"key":"10.1016\/j.artmed.2024.102995_b32","doi-asserted-by":"crossref","DOI":"10.1063\/5.0055600","article-title":"Uncovering near-wall blood flow from sparse data with physics-informed neural networks","volume":"33","author":"Arzani","year":"2021","journal-title":"Phys Fluids"},{"key":"10.1016\/j.artmed.2024.102995_b33","doi-asserted-by":"crossref","first-page":"3957","DOI":"10.1007\/s00366-022-01709-3","article-title":"Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps","volume":"38","author":"Ruiz Herrera","year":"2022","journal-title":"Eng Comput"},{"key":"10.1016\/j.artmed.2024.102995_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2019.112732","article-title":"Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data","volume":"361","author":"Sun","year":"2020","journal-title":"Comput Methods Appl Mech Engrg"},{"key":"10.1016\/j.artmed.2024.102995_b35","doi-asserted-by":"crossref","first-page":"11577","DOI":"10.1038\/s41598-024-62117-9","article-title":"Strategies for multi-case physics-informed neural networks for tube flows: a study using 2d flow scenarios","volume":"14","author":"Wong","year":"2024","journal-title":"Sci Rep"},{"key":"10.1016\/j.artmed.2024.102995_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102066","article-title":"Personalising left-ventricular biophysical models of the heart using parametric physics-informed neural networks","volume":"71","author":"Buoso","year":"2021","journal-title":"Med Image Anal"},{"key":"10.1016\/j.artmed.2024.102995_b37","doi-asserted-by":"crossref","DOI":"10.1115\/1.4055835","article-title":"Neural network approaches for soft biological tissue and organ simulations","volume":"144","author":"Sacks","year":"2022","journal-title":"J Biomech Eng"},{"key":"10.1016\/j.artmed.2024.102995_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2023.116351","article-title":"Physics-informed graph neural network emulation of soft-tissue mechanics","volume":"417","author":"Dalton","year":"2023","journal-title":"Comput Methods Appl Mech Engrg"},{"issue":"2","key":"10.1016\/j.artmed.2024.102995_b39","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/TBME.1985.325439","article-title":"Reduced models of arterial systems","volume":"BME-32","author":"Toy","year":"1985","journal-title":"IEEE Trans Biomed Eng"},{"issue":"03","key":"10.1016\/j.artmed.2024.102995_b40","doi-asserted-by":"crossref","DOI":"10.1142\/S0219876218420045","article-title":"A method to personalize the lumped parameter model of coronary artery","volume":"16","author":"Li","year":"2019","journal-title":"Int J Comput Methods"},{"key":"10.1016\/j.artmed.2024.102995_b41","article-title":"Lumped parameter model based surgical planning for CABG","volume":"2","author":"Mao","year":"2019","journal-title":"Med Nov Technol Devices"},{"key":"10.1016\/j.artmed.2024.102995_b42","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1007\/s12265-020-09953-y","article-title":"Lumped-parameter circuit platform for simulating typical cases of pulmonary hypertensions from point of hemodynamics","volume":"13","author":"Tang","year":"2020","journal-title":"J Cardiovasc Transl Res"},{"key":"10.1016\/j.artmed.2024.102995_b43","doi-asserted-by":"crossref","first-page":"2977","DOI":"10.1007\/s11831-021-09685-5","article-title":"The critical role of lumped parameter models in patient-specific cardiovascular simulations","volume":"29","author":"Garber","year":"2022","journal-title":"Arch Comput Methods Eng"},{"issue":"1","key":"10.1016\/j.artmed.2024.102995_b44","doi-asserted-by":"crossref","first-page":"H146","DOI":"10.1152\/ajpheart.1991.260.1.H146","article-title":"Hemodynamic consequences of ventricular interaction as assessed by model analysis","volume":"260","author":"Santamore","year":"1991","journal-title":"Am J Physiology-Heart Circ Physiol"},{"key":"10.1016\/j.artmed.2024.102995_b45","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s12265-018-9793-1","article-title":"Predicting the time course of ventricular dilation and thickening using a rapid compartmental model","volume":"11","author":"Witzenburg","year":"2018","journal-title":"J Cardiovasc Transl Res"},{"key":"10.1016\/j.artmed.2024.102995_b46","series-title":"Applied mathematical models in human physiology","first-page":"137","article-title":"6. A cardiovascular model","author":"Danielsen","year":"2004"},{"key":"10.1016\/j.artmed.2024.102995_b47","doi-asserted-by":"crossref","DOI":"10.1002\/cnm.2622","article-title":"A global multiscale mathematical model for the human circulation with emphasis on the venous system","volume":"30","author":"M\u00fcller","year":"2014","journal-title":"Int J Numer Methods Biomed Eng"},{"key":"10.1016\/j.artmed.2024.102995_b48","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2023.107908","article-title":"Simulation of coronary capillary transit time based on full vascular model of the heart","volume":"243","author":"Wang","year":"2024","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.artmed.2024.102995_b49","doi-asserted-by":"crossref","DOI":"10.1016\/j.jcp.2021.110242","article-title":"A method for representing periodic functions and enforcing exactly periodic boundary conditions with deep neural networks","volume":"435","author":"Dong","year":"2021","journal-title":"J Comput Phys"},{"issue":"6","key":"10.1016\/j.artmed.2024.102995_b50","doi-asserted-by":"crossref","first-page":"B1105","DOI":"10.1137\/21M1397908","article-title":"Physics-informed neural networks with hard constraints for inverse design","volume":"43","author":"Lu","year":"2021","journal-title":"SIAM J Sci Comput"},{"issue":"2","key":"10.1016\/j.artmed.2024.102995_b51","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1080\/00401706.1987.10488205","article-title":"Large sample properties of simulations using latin hypercube sampling","volume":"29","author":"Stein","year":"1987","journal-title":"Technometrics"},{"key":"10.1016\/j.artmed.2024.102995_b52","series-title":"Advances in neural information processing systems 32","first-page":"8024","article-title":"PyTorch: An imperative style, high-performance deep learning library","author":"Paszke","year":"2019"},{"key":"10.1016\/j.artmed.2024.102995_b53","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"Int Conf Learn Represent"},{"year":"2018","series-title":"Neural networks and deep learning: A textbook","author":"Aggarwal","key":"10.1016\/j.artmed.2024.102995_b54"},{"issue":"1","key":"10.1016\/j.artmed.2024.102995_b55","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/S0378-4754(00)00270-6","article-title":"Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates","volume":"55","author":"Sobol\u2019","year":"2001","journal-title":"Math Comput Simulation"},{"issue":"2","key":"10.1016\/j.artmed.2024.102995_b56","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/S0010-4655(02)00280-1","article-title":"Making best use of model evaluations to compute sensitivity indices","volume":"145","author":"Saltelli","year":"2002","journal-title":"Comput Phys Comm"},{"issue":"2","key":"10.1016\/j.artmed.2024.102995_b57","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.cpc.2009.09.018","article-title":"Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index","volume":"181","author":"Saltelli","year":"2010","journal-title":"Comput Phys Comm"},{"issue":"4","key":"10.1016\/j.artmed.2024.102995_b58","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1016\/j.cpc.2010.12.039","article-title":"From screening to quantitative sensitivity analysis. a unified approach","volume":"182","author":"Campolongo","year":"2011","journal-title":"Comput Phys Comm"},{"key":"10.1016\/j.artmed.2024.102995_b59","series-title":"Monte Carlo and quasi-Monte Carlo methods","article-title":"On dropping the first Sobol\u2019 point","author":"Owen","year":"2020"},{"issue":"9","key":"10.1016\/j.artmed.2024.102995_b60","doi-asserted-by":"crossref","DOI":"10.21105\/joss.00097","article-title":"SALib: An open-source python library for sensitivity analysis","volume":"2","author":"Herman","year":"2017","journal-title":"J Open Source Softw"},{"key":"10.1016\/j.artmed.2024.102995_b61","first-page":"18155","article-title":"Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses","volume":"4","author":"Iwanaga","year":"2022","journal-title":"Socio-Environmen Syst Model"},{"issue":"3","key":"10.1016\/j.artmed.2024.102995_b62","doi-asserted-by":"crossref","first-page":"H1037","DOI":"10.1152\/ajpheart.00549.2020","article-title":"Role of coronary flow regulation and cardiac-coronary coupling in mechanical dyssynchrony associated with right ventricular pacing","volume":"320","author":"Fan","year":"2021","journal-title":"Am J Physiology-Heart Circ Physiol"},{"key":"10.1016\/j.artmed.2024.102995_b63","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.105050","article-title":"Optimization of cardiac resynchronization therapy based on a cardiac electromechanics-perfusion computational model","volume":"141","author":"Fan","year":"2022","journal-title":"Comput Biol Med"},{"issue":"1","key":"10.1016\/j.artmed.2024.102995_b64","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1161\/01.RES.36.1.185","article-title":"Dobutamine: development of a new catecholamine to selectively increase cardiac contractility","volume":"36","author":"Tuttle","year":"1975","journal-title":"Circ Res"},{"key":"10.1016\/j.artmed.2024.102995_b65","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1007\/BF02058521","article-title":"Influence of ventricular contractility on non-work-related myocardial oxygen consumption","volume":"3","author":"Burkhoff","year":"1987","journal-title":"Heart Vessels"},{"issue":"4","key":"10.1016\/j.artmed.2024.102995_b66","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1002\/ehf2.12248","article-title":"Effects of intravenous home dobutamine in palliative end-stage heart failure on quality of life, heart failure hospitalization, and cost expenditure","volume":"5","author":"Martens","year":"2018","journal-title":"ESC Heart Fail"},{"key":"10.1016\/j.artmed.2024.102995_b67","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","article-title":"Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces","volume":"11","author":"Storn","year":"1997","journal-title":"J Global Optim"},{"year":"2005","series-title":"Differential evolution: A practical approach to global optimization (natural computing series)","author":"Price","key":"10.1016\/j.artmed.2024.102995_b68"},{"key":"10.1016\/j.artmed.2024.102995_b69","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nature Methods"},{"issue":"12","key":"10.1016\/j.artmed.2024.102995_b70","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1161\/01.CIR.0000058171.62847.90","article-title":"Noninvasive assessment of left ventricular force-frequency relationships using tissue Doppler-derived isovolumic acceleration","volume":"107","author":"Vogel","year":"2003","journal-title":"Circulation"},{"key":"10.1016\/j.artmed.2024.102995_b71","doi-asserted-by":"crossref","first-page":"1422","DOI":"10.1161\/01.CIR.76.6.1422","article-title":"Comparative influence of load versus inotropic states on indexes of ventricular contractility: experimental and theoretical analysis based on pressure-volume relationships","volume":"76 6","author":"Kass","year":"1987","journal-title":"Circulation"},{"key":"10.1016\/j.artmed.2024.102995_b72","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/BF02650364","article-title":"Use of a conductance catheter to detect increased left ventricular inotropic state by end-systolic pressure-volume analysis","volume":"84","author":"Leatherman","year":"1989","journal-title":"Basic Res Cardiol"},{"issue":"1","key":"10.1016\/j.artmed.2024.102995_b73","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1161\/01.CIR.83.1.202","article-title":"Single-beat estimation of the slope of the end-systolic pressure-volume relation in the human left ventricle","volume":"83","author":"Takeuchi","year":"1991","journal-title":"Circulation"},{"issue":"7","key":"10.1016\/j.artmed.2024.102995_b74","doi-asserted-by":"crossref","first-page":"2028","DOI":"10.1016\/S0735-1097(01)01651-5","article-title":"Noninvasive single-beat determination of left ventricular end-systolic elastance in humans","volume":"38","author":"Chen","year":"2001","journal-title":"J Am Coll Cardiol"},{"key":"10.1016\/j.artmed.2024.102995_b75","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J Mach Learn Res"},{"issue":"4","key":"10.1016\/j.artmed.2024.102995_b76","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3447582","article-title":"A comprehensive survey of neural architecture search: Challenges and solutions","volume":"54","author":"Ren","year":"2021","journal-title":"ACM Comput Surv"},{"issue":"10","key":"10.1016\/j.artmed.2024.102995_b77","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J Mach Learn Res"},{"year":"2023","series-title":"The CMA evolution strategy: A tutorial","author":"Hansen","key":"10.1016\/j.artmed.2024.102995_b78"},{"issue":"1","key":"10.1016\/j.artmed.2024.102995_b79","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1109\/JPROC.2015.2494218","article-title":"Taking the human out of the loop: A review of Bayesian optimization","volume":"104","author":"Shahriari","year":"2016","journal-title":"Proc IEEE"},{"key":"10.1016\/j.artmed.2024.102995_b80","series-title":"Proceedings of the 34th international conference on neural information processing systems","article-title":"BOTORCH: a framework for efficient monte-carlo Bayesian optimization","author":"Balandat","year":"2020"},{"year":"2023","series-title":"Tree-structured parzen estimator: Understanding its algorithm components and their roles for better empirical performance","author":"Watanabe","key":"10.1016\/j.artmed.2024.102995_b81"},{"key":"10.1016\/j.artmed.2024.102995_b82","series-title":"Automated machine learning: methods, systems, challenges","first-page":"3","article-title":"Hyperparameter optimization","author":"Feurer","year":"2019"},{"year":"2022","series-title":"Auto-PINN: Understanding and optimizing physics-informed neural architecture","author":"Wang","key":"10.1016\/j.artmed.2024.102995_b83"},{"key":"10.1016\/j.artmed.2024.102995_b84","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.110646","article-title":"Identifying optimal architectures of physics-informed neural networks by evolutionary strategy","volume":"146","author":"Kaplarevi\u0107-Mali\u0161i\u0107","year":"2023","journal-title":"Appl Soft Comput"}],"container-title":["Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0933365724002379?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0933365724002379?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,11,10]],"date-time":"2024-11-10T21:09:54Z","timestamp":1731272994000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0933365724002379"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11]]},"references-count":84,"alternative-id":["S0933365724002379"],"URL":"https:\/\/doi.org\/10.1016\/j.artmed.2024.102995","relation":{},"ISSN":["0933-3657"],"issn-type":[{"type":"print","value":"0933-3657"}],"subject":[],"published":{"date-parts":[[2024,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Rapid estimation of left ventricular contractility with a physics-informed neural network inverse modeling approach","name":"articletitle","label":"Article Title"},{"value":"Artificial Intelligence in Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.artmed.2024.102995","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"102995"}}