{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T21:04:01Z","timestamp":1784667841018,"version":"3.55.0"},"reference-count":69,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,4,3]],"date-time":"2027-04-03T00:00:00Z","timestamp":1806710400000},"content-version":"am","delay-in-days":337,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"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","award":["1R21HL113857"],"award-info":[{"award-number":["1R21HL113857"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers in Biology and Medicine"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.compbiomed.2026.111661","type":"journal-article","created":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T07:18:37Z","timestamp":1775200717000},"page":"111661","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Generation of virtual abdominal aortic aneurysm shape evolution using a physics-based G&R model and its applications to aneurysm growth prediction"],"prefix":"10.1016","volume":"208","author":[{"given":"Faeze","family":"Jahani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenxiang","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Malikeh","family":"Nabaei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2007-339X","authenticated-orcid":false,"given":"Seungik","family":"Baek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compbiomed.2026.111661_bib1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.jvs.2017.10.044","article-title":"The society for vascular surgery practice guidelines on the care of patients with an abdominal aortic aneurysm","volume":"67","author":"Chaikof","year":"2018","journal-title":"J. Vasc. Surg."},{"key":"10.1016\/j.compbiomed.2026.111661_bib2","article-title":"Geometric and biomechanical modeling aided by machine learning improves the prediction of growth and rupture of small abdominal aortic aneurysms","volume":"11","author":"Liljeqvist","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compbiomed.2026.111661_bib3","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.compbiomed.2026.111661_bib4","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1177\/17085381221077821","article-title":"Prediction of abdominal aortic aneurysm growth by artificial intelligence taking into account clinical, biologic, morphologic, and biomechanical variables","volume":"31","author":"Kontopodis","year":"2023","journal-title":"Vascular"},{"key":"10.1016\/j.compbiomed.2026.111661_bib5","doi-asserted-by":"crossref","first-page":"3390","DOI":"10.1038\/s41598-024-53459-5","article-title":"An artificial intelligence based abdominal aortic aneurysm prognosis classifier to predict patient outcomes","volume":"14","author":"Chung","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compbiomed.2026.111661_bib6","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1038\/s42256-023-00629-1","article-title":"Synthetic data accelerates the development of generalizable learning-based algorithms for X-ray image analysis","volume":"5","author":"Gao","year":"2023","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.compbiomed.2026.111661_bib7","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1038\/s41746-023-00927-3","article-title":"Harnessing the power of synthetic data in healthcare: innovation, application, and privacy","volume":"6","author":"Giuffr\u00e8","year":"2023","journal-title":"npj Digit. Med."},{"key":"10.1016\/j.compbiomed.2026.111661_bib8","series-title":"Functional Imaging and Modeling of the Heart","first-page":"356","article-title":"Implicit neural representations for modeling of abdominal aortic aneurysm progression","author":"Alblas","year":"2023"},{"key":"10.1016\/j.compbiomed.2026.111661_bib9","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-40139-z","article-title":"Characterization of small abdominal aortic aneurysms' growth status using spatial pattern analysis of aneurismal hemodynamics","volume":"13","author":"Rezaeitaleshmahalleh","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compbiomed.2026.111661_bib10","unstructured":"C. Peng, J. Luan, Y. Zhang, F. He, T. Yuan, W. Fu, W. He, Y. Shi, Quantitative relationship between gain factors and growth rate in the growth and remodeling process of abdominal aortic aneurysms, Available at: SSRN 4639467 (n.d.), 10.2139\/ssrn.4639467."},{"key":"10.1016\/j.compbiomed.2026.111661_bib11","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107052","article-title":"Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate","volume":"162","author":"Geronzi","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2026.111661_bib12","doi-asserted-by":"crossref","DOI":"10.1098\/rspa.2012.0556","article-title":"Parametric study of effects of collagen turnover on the natural history of abdominal aortic aneurysms","volume":"469","author":"Wilson","year":"2013","journal-title":"Proc. R. Soc. A"},{"key":"10.1016\/j.compbiomed.2026.111661_bib13","doi-asserted-by":"crossref","DOI":"10.1115\/1.4031019","article-title":"Computational growth and remodeling of abdominal aortic aneurysms constrained by the spine","volume":"137","author":"Farsad","year":"2015","journal-title":"J. Biomech. Eng."},{"key":"10.1016\/j.compbiomed.2026.111661_bib14","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1007\/s10237-016-0859-1","article-title":"Homogenized constrained mixture models for anisotropic volumetric growth and remodeling","volume":"16","author":"Braeu","year":"2017","journal-title":"Biomech. Model. Mechanobiol."},{"key":"10.1016\/j.compbiomed.2026.111661_bib15","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1008273","article-title":"Numerical knockouts\u2013in silico assessment of factors predisposing to thoracic aortic aneurysms","volume":"16","author":"Latorre","year":"2020","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.compbiomed.2026.111661_bib16","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1007\/s10237-024-01828-4","article-title":"Preliminary establishment and validation of the inversion method for growth and remodeling parameters of patient-specific abdominal aortic aneurysm","volume":"23","author":"Peng","year":"2024","journal-title":"Biomech. Model. Mechanobiol."},{"key":"10.1016\/j.compbiomed.2026.111661_bib17","series-title":"Computational Biomechanics for Medicine","first-page":"13","article-title":"Data-guided growth and remodeling model of abdominal aortic aneurysm accounting for the bio-chemical effects of intraluminal thrombus","author":"Farsad","year":"2015"},{"key":"10.1016\/j.compbiomed.2026.111661_bib18","doi-asserted-by":"crossref","first-page":"235","DOI":"10.3389\/fphy.2019.00235","article-title":"A deep learning approach to predict abdominal aortic aneurysm expansion using longitudinal data","volume":"7","author":"Jiang","year":"2020","journal-title":"Front. Phys."},{"key":"10.1016\/j.compbiomed.2026.111661_bib19","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104394","article-title":"Machine learning approaches to surrogate multifidelity growth and remodeling models for efficient abdominal aortic aneurysmal applications","volume":"133","author":"Jiang","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2026.111661_bib20","article-title":"Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces","author":"Alblas","year":"2025","journal-title":"arXiv Preprint arXiv:2506.08729"},{"key":"10.1016\/j.compbiomed.2026.111661_bib21","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1080\/10255842.2010.495344","article-title":"A finite element model of stress-mediated vascular adaptation: application to abdominal aortic aneurysms","volume":"9","author":"Zeinali-Davarani","year":"2011","journal-title":"Comput. Methods Biomech. Biomed. Eng."},{"key":"10.1016\/j.compbiomed.2026.111661_bib22","doi-asserted-by":"crossref","first-page":"1238","DOI":"10.1016\/j.jvs.2022.03.879","article-title":"Evaluation of aortic tortuosity as a negative predictor of abdominal aortic aneurysm rupture","volume":"76","author":"Hejazi","year":"2022","journal-title":"J. Vasc. Surg."},{"issue":"2","key":"10.1016\/j.compbiomed.2026.111661_bib23","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.jvs.2008.08.064","article-title":"Vessel asymmetry as an additional diagnostic tool in the assessment of abdominal aortic aneurysms","volume":"49","author":"Doyle","year":"2009","journal-title":"J. Vasc. Surg."},{"key":"10.1016\/j.compbiomed.2026.111661_bib24","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1007\/s10439-013-0786-6","article-title":"The role of geometric and biomechanical factors in abdominal aortic aneurysm rupture risk assessment","volume":"41","author":"Raut","year":"2013","journal-title":"Ann. Biomed. Eng."},{"key":"10.1016\/j.compbiomed.2026.111661_bib25","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1142\/S0218202502001714","article-title":"A constrained mixture model for growth and remodeling of soft tissues","volume":"12","author":"Humphrey","year":"2002","journal-title":"Math. Model Methods Appl. Sci."},{"key":"10.1016\/j.compbiomed.2026.111661_bib26","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s10659-020-09809-1","article-title":"Constrained mixture models of soft tissue growth and remodeling \u2013 twenty years after","volume":"145","author":"Humphrey","year":"2021","journal-title":"J. Elasticity"},{"key":"10.1016\/j.compbiomed.2026.111661_bib27","series-title":"Computational Modeling in Biomechanics","first-page":"253","article-title":"Computational modeling of growth and remodeling in biological soft tissues: application to arterial mechanics","author":"Baek","year":"2010"},{"key":"10.1016\/j.compbiomed.2026.111661_bib28","doi-asserted-by":"crossref","first-page":"3583","DOI":"10.1016\/j.cma.2008.09.013","article-title":"A computational framework for fluid-solid-growth modeling in cardiovascular simulations","volume":"198","author":"Figueroa","year":"2009","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"10.1016\/j.compbiomed.2026.111661_bib29","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1007\/s10237-010-0265-z","article-title":"A constrained mixture model for developing mouse aorta","volume":"10","author":"Wagenseil","year":"2011","journal-title":"Biomech. Model. Mechanobiol."},{"key":"10.1016\/j.compbiomed.2026.111661_bib30","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.jmps.2018.12.013","article-title":"Mechanobiological stability of biological soft tissues","volume":"125","author":"Latorre","year":"2019","journal-title":"J. Mech. Phys. Solid."},{"key":"10.1016\/j.compbiomed.2026.111661_bib31","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.mechrescom.2012.01.008","article-title":"Medical image-based simulation of abdominal aortic aneurysm growth","volume":"42","author":"Zeinali-Davarani","year":"2012","journal-title":"Mech. Res. Commun."},{"key":"10.1016\/j.compbiomed.2026.111661_bib32","first-page":"33","article-title":"An inverse optimization approach toward testing different hypotheses of vascular homeostasis using image-based models","volume":"3","author":"Zeinali-Davarani","year":"2011","journal-title":"Int. J. Struct. Changes Solids"},{"key":"10.1016\/j.compbiomed.2026.111661_bib33","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1007\/s10237-010-0266-y","article-title":"Identification of in vivo material and geometric parameters of a human aorta: toward patient-specific modeling of abdominal aortic aneurysm","volume":"10","author":"Zeinali-Davarani","year":"2011","journal-title":"Biomech. Model. Mechanobiol."},{"key":"10.1016\/j.compbiomed.2026.111661_bib34","series-title":"Patient-Specific Prediction of Abdominal Aortic Aneurysm Expansion Using Efficient Patient-based Machine Learning Approaches","author":"Jiang","year":"2022"},{"key":"10.1016\/j.compbiomed.2026.111661_bib35","doi-asserted-by":"crossref","first-page":"3070","DOI":"10.1016\/j.cma.2006.06.018","article-title":"Theory of small on large: potential utility in computations of fluid-solid interactions in arteries","volume":"196","author":"Baek","year":"2007","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"10.1016\/j.compbiomed.2026.111661_bib36","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s10659-005-9004-6","article-title":"Competition between radial expansion and thickening in the enlargement of an intracranial saccular aneurysm","volume":"80","author":"Baek","year":"2005","journal-title":"J. Elasticity"},{"key":"10.1016\/j.compbiomed.2026.111661_bib37","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1115\/1.2132374","article-title":"A theoretical model of enlarging intracranial fusiform aneurysms","volume":"128","author":"Baek","year":"2006","journal-title":"J. Biomech. Eng."},{"key":"10.1016\/j.compbiomed.2026.111661_bib38","series-title":"Modeling and Simulation in Science, Engineering and Technology","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/978-0-8176-4411-6_3","article-title":"Theoretical modeling of enlarging intracranial aneurysms","author":"Baek","year":"2007"},{"key":"10.1016\/j.compbiomed.2026.111661_bib39","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1016\/j.medengphy.2015.04.011","article-title":"On growth measurements of abdominal aortic aneurysms using maximally inscribed spheres","volume":"37","author":"Gharahi","year":"2015","journal-title":"Med. Eng. Phys."},{"key":"10.1016\/j.compbiomed.2026.111661_bib40","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103620","article-title":"Predicting abdominal aortic aneurysm growth using patient-oriented growth models with two-step Bayesian inference","volume":"117","author":"Akkoyun","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2026.111661_bib41","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1016\/j.jvs.2012.11.070","article-title":"Multidimensional growth measurements of abdominal aortic aneurysms","volume":"58","author":"Martufi","year":"2013","journal-title":"J. Vasc. Surg."},{"key":"10.1016\/j.compbiomed.2026.111661_bib42","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106256","article-title":"Defining a master curve of abdominal aortic aneurysm growth and its potential utility of clinical management","volume":"208","author":"Akkoyun","year":"2021","journal-title":"Comput. Methods Progr. Biomed."},{"key":"10.1016\/j.compbiomed.2026.111661_bib43","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1080\/00401706.1989.10488474","article-title":"Designs for computer experiments","volume":"31","author":"Sacks","year":"1989","journal-title":"Technometrics"},{"key":"10.1016\/j.compbiomed.2026.111661_bib44","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s00366-018-0590-x","article-title":"Gradient-enhanced kriging for high-dimensional problems","volume":"35","author":"Bouhlel","year":"2019","journal-title":"Eng. Comput."},{"key":"10.1016\/j.compbiomed.2026.111661_bib45","doi-asserted-by":"crossref","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"10.1016\/j.compbiomed.2026.111661_bib46","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/978-1-0716-3195-9_4","article-title":"Recurrent neural networks (RNNs): architectures, training tricks, and introduction to influential research","author":"Das","year":"2023","journal-title":"Mach.Learn. Brain Disorders"},{"key":"10.1016\/j.compbiomed.2026.111661_bib47","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"10.1016\/j.compbiomed.2026.111661_bib48","article-title":"Learning phrase representations using RNN encoder-decoder for statistical machine translation","author":"Cho","year":"2014","journal-title":"arXiv Preprint arXiv:1406.1078"},{"key":"10.1016\/j.compbiomed.2026.111661_bib49","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1114\/1.202","article-title":"In vivo three-dimensional surface geometry of abdominal aortic aneurysms","volume":"27","author":"Sacks","year":"1999","journal-title":"Ann. Biomed. Eng."},{"key":"10.1016\/j.compbiomed.2026.111661_bib50","doi-asserted-by":"crossref","first-page":"2537","DOI":"10.1109\/JBHI.2019.2896034","article-title":"Patient-specific prediction of abdominal aortic aneurysm expansion using Bayesian calibration","volume":"23","author":"Zhang","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.compbiomed.2026.111661_bib51","doi-asserted-by":"crossref","first-page":"3124","DOI":"10.1007\/s10439-010-0067-6","article-title":"A comparison of diameter, wall stress, and rupture potential index for abdominal aortic aneurysm rupture risk prediction","volume":"38","author":"Maier","year":"2010","journal-title":"Ann. Biomed. Eng."},{"key":"10.1016\/j.compbiomed.2026.111661_bib52","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0242097","article-title":"Biomechanical rupture risk assessment of abdominal aortic aneurysms using clinical data: a patient-specific, probabilistic framework and comparative case-control study","volume":"15","author":"Bruder","year":"2020","journal-title":"PLoS One"},{"key":"10.1016\/j.compbiomed.2026.111661_bib53","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1007\/s10237-019-01184-8","article-title":"Patient-specific predictions of aneurysm growth and remodeling in the ascending thoracic aorta using the homogenized constrained mixture model","volume":"18","author":"Mousavi","year":"2019","journal-title":"Biomech. Model. Mechanobiol."},{"key":"10.1016\/j.compbiomed.2026.111661_bib54","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1007\/s10237-024-01915-6","article-title":"Fluid\u2013structure\u2013growth modeling in ascending aortic aneurysm: capability to reproduce a patient case","volume":"24","author":"Yan","year":"2025","journal-title":"Biomech. Model. Mechanobiol."},{"key":"10.1016\/j.compbiomed.2026.111661_bib55","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0187421","article-title":"Biomechanical changes during abdominal aortic aneurysm growth","volume":"12","author":"Stevens","year":"2017","journal-title":"PLoS One"},{"key":"10.1016\/j.compbiomed.2026.111661_bib56","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1177\/1358863X241309836","article-title":"Modulation of arterial wall remodeling by mechanical stress: focus on abdominal aortic aneurysm","volume":"30","author":"Qiao","year":"2025","journal-title":"Vasc. Med."},{"key":"10.1016\/j.compbiomed.2026.111661_bib57","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.avsg.2021.12.015","article-title":"Optimal timing of surveillance ultrasounds in small aortic aneurysms","volume":"83","author":"Rowse","year":"2022","journal-title":"Ann. Vasc. Surg."},{"issue":"8","key":"10.1016\/j.compbiomed.2026.111661_bib58","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.3348\/kjr.2018.0927","article-title":"Endovascular aneurysm repair for abdominal aortic aneurysm: a comprehensive review","volume":"20","author":"Kim","year":"2019","journal-title":"Korean J. Radiol."},{"key":"10.1016\/j.compbiomed.2026.111661_bib59","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-026-41340-6","article-title":"Geometry quantification for growth assessment of abdominal aortic aneurysms under surveillance","author":"Restrepo","year":"2026","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compbiomed.2026.111661_bib60","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1109\/TMI.2022.3206142","article-title":"Deep learning on multiphysical features and hemodynamic modeling for abdominal aortic aneurysm growth prediction","volume":"42","author":"Kim","year":"2023","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.compbiomed.2026.111661_bib61","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1007\/s12265-023-10404-7","article-title":"Radiomic-based textural analysis of intraluminal thrombus in aortic abdominal aneurysms: a demonstration of automated workflow","volume":"16","author":"Rezaeitaleshmahalleh","year":"2023","journal-title":"J. Cardiovasc. Transl. Res."},{"key":"10.1016\/j.compbiomed.2026.111661_bib62","doi-asserted-by":"crossref","first-page":"1502","DOI":"10.1007\/s10439-015-1461-x","article-title":"Association of intraluminal thrombus, hemodynamic forces, and abdominal aortic aneurysm expansion using longitudinal CT images","volume":"44","author":"Zambrano","year":"2016","journal-title":"Ann. Biomed. Eng."},{"key":"10.1016\/j.compbiomed.2026.111661_bib63","doi-asserted-by":"crossref","DOI":"10.1002\/cnm.3555","article-title":"Association of vortical structures and hemodynamic parameters for regional thrombus accumulation in abdominal aortic aneurysms","volume":"38","author":"Zambrano","year":"2022","journal-title":"Int. J. Numer. Methods Biomed. Eng."},{"key":"10.1016\/j.compbiomed.2026.111661_bib64","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s12968-022-00848-2","article-title":"Wall shear stress and relative residence time as potential risk factors for abdominal aortic aneurysms in males: a 4D flow cardiovascular magnetic resonance case\u00e2control study","volume":"24","author":"Trenti","year":"2022","journal-title":"J. Cardiovasc. Magn. Reson."},{"key":"10.1016\/j.compbiomed.2026.111661_bib65","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.medengphy.2010.09.012","article-title":"Simulation of abdominal aortic aneurysm growth with updating hemodynamic loads using a realistic geometry","volume":"33","author":"Sheidaei","year":"2011","journal-title":"Med. Eng. Phys."},{"key":"10.1016\/j.compbiomed.2026.111661_bib66","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2024.117259","article-title":"FSGe: a fast and strongly-coupled 3D fluid\u2013solid-growth interaction method","volume":"431","author":"Pfaller","year":"2024","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"10.1016\/j.compbiomed.2026.111661_bib67","series-title":"Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers","first-page":"79","article-title":"Going off-grid: continuous implicit neural representations for 3D vascular modeling","author":"Alblas","year":"2022"},{"key":"10.1016\/j.compbiomed.2026.111661_bib68","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2025.106138","article-title":"Digital twins in cardiovascular disease: a scoping review","volume":"206","author":"Zou","year":"2026","journal-title":"Int. J. Med. Inf."},{"key":"10.1016\/j.compbiomed.2026.111661_bib69","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2021.713118","article-title":"Clinically-driven virtual patient cohorts generation: an application to aorta","volume":"12","author":"Romero","year":"2021","journal-title":"Front. Physiol."}],"container-title":["Computers in Biology and Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482526002258?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482526002258?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T20:42:03Z","timestamp":1784666523000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0010482526002258"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":69,"alternative-id":["S0010482526002258"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiomed.2026.111661","relation":{},"ISSN":["0010-4825"],"issn-type":[{"value":"0010-4825","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Generation of virtual abdominal aortic aneurysm shape evolution using a physics-based G&R model and its applications to aneurysm growth prediction","name":"articletitle","label":"Article Title"},{"value":"Computers in Biology and Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiomed.2026.111661","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111661"}}