{"id":52248,"date":"2025-08-06T08:10:48","date_gmt":"2025-08-06T12:10:48","guid":{"rendered":"https:\/\/engineering.jhu.edu\/case\/?post_type=news&p=52248"},"modified":"2025-08-14T14:46:28","modified_gmt":"2025-08-14T18:46:28","slug":"virtual-hearts-get-smarter","status":"publish","type":"news","link":"https:\/\/engineering.jhu.edu\/case\/news\/virtual-hearts-get-smarter\/","title":{"rendered":"Virtual Hearts Get Smarter"},"content":{"rendered":"

Digital twins\u2014virtual replicas that simulate a patient\u2019s unique cardiac structure and function\u2014hold promise for predicting heart problems, but they aren\u2019t yet reliable enough for clinical use. Two Johns Hopkins civil and systems engineers are applying their expertise in computational modeling to change that, working to make digital twins of human hearts accurate enough to reliably predict which patients will suffer dangerous heart rhythm disruptions that can lead to sudden cardiac death.<\/span>\u00a0<\/span><\/p>\n

Supported by the National Science Foundation, <\/span>Dimitris Giovanis<\/span><\/a>, an assistant research professor, and <\/span>Somdatta Goswami<\/span><\/a>, an assistant professor, are co-PIs on a team that is developing new ways to correct errors in existing heart digital twins. Led by principal investigator <\/span>Mauro Maggioni<\/span><\/a> and co-principal investigators <\/span>Natalia Trayanova<\/span><\/a> and <\/span>Yannis Kevrekidis<\/span><\/a>, they aim to transform the twins from promising research models to clinical tools that could improve procedures like cardiac ablation, where surgeons precisely destroy small areas of heart tissue to treat dangerous arrhythmias, and ultimately save lives. <\/span>\u00a0<\/span><\/p>\n

\u201cSudden cardiac death is the leading cause of natural deaths worldwide, and it can sometimes happen within just an hour of symptom onset. A major challenge in effective prevention is figuring out which patients are at risk. If we can improve how we predict and treat heart conditions using digital models, we could save lives and improve care for people living with serious heart problems,\u201d said Giovanis.<\/span>\u00a0<\/span><\/p>\n

The team\u2019s approach starts with a 3D digital heart model created using patient scans and standard modeling. Their key innovation comes next: using advanced mathematics, digital simulations, machine learning, and patient data to fine-tune the models. Machine learning compensates for data limitations and biological uncertainties, while advanced mathematical modeling corrects errors in the replica. They say that the resulting digital twin will be better at predicting what\u2019s happening inside a patient\u2019s heart.<\/span>\u00a0<\/span><\/p>\n

\u00a0\u201cEnhancing cardiac digital twins will make ablation procedures\u2014removal of tissue\u2014for people with heart arrhythmias more accurate and efficient. Reliable, patient-specific replicas will allow doctors to plan precisely where and how to intervene, potentially shaving hours off of procedures and improving outcomes for those with heart conditions,\u201d says Goswami.<\/span>\u00a0<\/span><\/p>\n

Improved digital twins could transform not only cardiac ablation but medical care more broadly.<\/span>\u00a0<\/span><\/p>\n

\u201cImagine doctors running any number of virtual experiments, testing new drugs, or planning surgeries on the twins\u2014without ever putting the actual patient at risk,\u201d said Giovanis.\u00a0<\/span>\u00a0<\/span><\/p>\n

He envisions this technology as a tool to answer questions about the progression of an individual\u2019s medical diagnosis, determine who\u2019s most likely to benefit from a therapy, and avoid unnecessary treatment.<\/span>\u00a0<\/span><\/p>\n

As the next step in the research, the team plans to test and refine their models using a larger group of patient samples. <\/span>\u00a0<\/span><\/p>\n

\u201cWe want to make digital twins ready for use in real clinics during everyday practice to support doctors in making better, faster, and more informed treatment decisions for patients,\u201d Giovanis said.<\/span>\u00a0<\/span><\/p>\n","protected":false},"template":"","class_list":["post-52248","news","type-news","status-publish","hentry","news_categories-decisionmaking-and-health","news_categories-research"],"acf":[],"yoast_head":"\nVirtual Hearts Get Smarter - Department of Civil & Systems Engineering<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/engineering.jhu.edu\/case\/news\/virtual-hearts-get-smarter\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Virtual Hearts Get Smarter - Department of Civil & Systems Engineering\" \/>\n<meta property=\"og:description\" content=\"Digital twins\u2014virtual replicas that simulate a patient\u2019s unique cardiac structure and function\u2014hold promise for predicting heart problems, but they aren\u2019t yet reliable enough for clinical use. Two Johns Hopkins civil…\" \/>\n<meta property=\"og:url\" content=\"https:\/\/engineering.jhu.edu\/case\/news\/virtual-hearts-get-smarter\/\" \/>\n<meta property=\"og:site_name\" content=\"Department of Civil & Systems Engineering\" \/>\n<meta property=\"article:modified_time\" content=\"2025-08-14T18:46:28+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"3 minutes\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Virtual Hearts Get Smarter - Department of Civil & Systems Engineering","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/engineering.jhu.edu\/case\/news\/virtual-hearts-get-smarter\/","og_locale":"en_US","og_type":"article","og_title":"Virtual Hearts Get Smarter - Department of Civil & Systems Engineering","og_description":"Digital twins\u2014virtual replicas that simulate a patient\u2019s unique cardiac structure and function\u2014hold promise for predicting heart problems, but they aren\u2019t yet reliable enough for clinical use. 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