[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120671-en":3,"doc-seo-120671-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120671,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning","Cardiovascular diseases remain a dominant driver of mortality, morbidity, and hospital admission worldwide. Advances in computational fluid dynamics, blood-flow imaging, and wearable sensing have expanded capabilities for analysis, visualization, and monitoring, but practical impact is limited by high computational cost, insufficient spatiotemporal resolution, and barriers to comprehensive data analysis. The work evaluates how machine learning—especially deep learning—can address these constraints and support translation.","Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning  \nCitation for published version (APA):  \nMoradi, H. , Al‑Hourani, A. , Concilia, G. , Khoshmanesh, F. , Nezami, F. R. , Needham, S. , Baratchi, S. , & Khoshmanesh, K. (2023) . Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning. Biophysical Reviews, 15(1), 19-33 . [https://doi.org/10.1007/s12551-022-01040-](https://doi.org/10.1007/s12551-022-01040-)[ ](https://doi.org/10.1007/s12551-022-01040-)7  \nDocument license:  \nTAVERNE  \nDOI:  \n10.1007/s12551-022-01040-7  \nDocument status and date:  \nPublished: 01/02/2023  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.tue.nl/taverne](www.tue.nl/taverne)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[openaccess@tue.nl](openaccess@tue.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 02. Aug. 2026  \nBiophysical Reviews (2023) 15:19–33  \n[https://doi.org/10.1007/s12551-022-01040-7](https://doi.org/10.1007/s12551-022-01040-7)  \nRecent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning  \nHamed Moradi1 · Akram Al‑Hourani2 · Gianmarco Concilia2 · Farnaz Khoshmanesh3 · Farhad R. Nezami4 · Scott Needham5 · Sara Baratchi6 · Khashayar Khoshmanesh2  \nReceived: 20 June 2022 / Accepted: 21 December 2022 / Published online: 10 January 2023  \n© International Union for Pure and Applied Biophysics (IUPAB) and Springer-Verlag GmbH Germany, part of Springer Nature 2023  \nAbstract  \nCardiovascular diseases are the leading cause of mortality, morbidity, and hospitalization around the world. Recent technological advances have facilitated analyzing, visualizing, and monitoring cardiovascular diseases using emerging computational fluid dynamics, blood flow imaging, and wearable sensing technologies. Yet, computational cost, limited spatiotemporal resolution, and obstacles for thorough data analysis have hindered the utility of such techniques to curb cardiovascular diseases. We herein discuss how leveraging machine learning techniques, and in particular deep learning methods, could overcome these limitations and offer promise for translation. We discuss the remarkable capacity of recently developed machine learning techniques to accelerate flow modeling, enhance the resolution while reduce the noise and ","cbCaianakPYLjUFd","https://ap.wps.com/l/cbCaianakPYLjUFd","pdf",5829004,1,16,"English","en",105,"# Abstract\n# Introduction\n# Keywords","[{\"question\":\"What motivates using machine learning for cardiovascular disease modeling and monitoring?\",\"answer\":\"Machine learning is highlighted as a way to overcome limits of existing computational and imaging approaches, such as high computational cost, limited spatiotemporal resolution, and difficulties in thorough data analysis.\"},{\"question\":\"Which technologies are discussed as enabling cardiovascular analysis and monitoring?\",\"answer\":\"The document points to computational fluid dynamics, blood-flow imaging methods, and wearable sensing technologies that generate diverse data for analysis and monitoring.\"},{\"question\":\"How do the proposed machine learning approaches improve cardiovascular imaging and detection?\",\"answer\":\"They are described as accelerating flow modeling and improving resolution while reducing noise and scanning time, enabling accurate detection of cardiovascular diseases from wearable-sensor data.\"}]","Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning | 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motivates using machine learning for cardiovascular disease modeling and monitoring?","Question",{"text":75,"@type":76},"Machine learning is highlighted as a way to overcome limits of existing computational and imaging approaches, such as high computational cost, limited spatiotemporal resolution, and difficulties in thorough data analysis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which technologies are discussed as enabling cardiovascular analysis and monitoring?",{"text":80,"@type":76},"The document points to computational fluid dynamics, blood-flow imaging methods, and wearable sensing technologies that generate diverse data for analysis and monitoring.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the proposed machine learning approaches improve cardiovascular imaging and detection?",{"text":84,"@type":76},"They are described as accelerating flow modeling and improving resolution while reducing noise and scanning time, enabling accurate detection of 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