Kaan Sel, Deen Osman, Fatemeh Zare, Sina Masoumi Shahrbabak, Laura Brattain, Jin-Oh Hahn, Omer T Inan, Ramakrishna Mukkamala, Jeffrey Palmer, David Paydarfar, Roderic I Pettigrew, Arshed A Quyyumi, Brian Telfer, Roozbeh Jafari
{"title":"为心血管健康打造数字双胞胎:从原理到临床影响。","authors":"Kaan Sel, Deen Osman, Fatemeh Zare, Sina Masoumi Shahrbabak, Laura Brattain, Jin-Oh Hahn, Omer T Inan, Ramakrishna Mukkamala, Jeffrey Palmer, David Paydarfar, Roderic I Pettigrew, Arshed A Quyyumi, Brian Telfer, Roozbeh Jafari","doi":"10.1161/JAHA.123.031981","DOIUrl":null,"url":null,"abstract":"<p><p>The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.</p>","PeriodicalId":54370,"journal":{"name":"Journal of the American Heart Association","volume":null,"pages":null},"PeriodicalIF":5.0000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.\",\"authors\":\"Kaan Sel, Deen Osman, Fatemeh Zare, Sina Masoumi Shahrbabak, Laura Brattain, Jin-Oh Hahn, Omer T Inan, Ramakrishna Mukkamala, Jeffrey Palmer, David Paydarfar, Roderic I Pettigrew, Arshed A Quyyumi, Brian Telfer, Roozbeh Jafari\",\"doi\":\"10.1161/JAHA.123.031981\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.</p>\",\"PeriodicalId\":54370,\"journal\":{\"name\":\"Journal of the American Heart Association\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the American Heart Association\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1161/JAHA.123.031981\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/8/1 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American Heart Association","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1161/JAHA.123.031981","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/1 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.
The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.
期刊介绍:
As an Open Access journal, JAHA - Journal of the American Heart Association is rapidly and freely available, accelerating the translation of strong science into effective practice.
JAHA is an authoritative, peer-reviewed Open Access journal focusing on cardiovascular and cerebrovascular disease. JAHA provides a global forum for basic and clinical research and timely reviews on cardiovascular disease and stroke. As an Open Access journal, its content is free on publication to read, download, and share, accelerating the translation of strong science into effective practice.