{"id":44528,"date":"2023-01-17T14:54:56","date_gmt":"2023-01-17T19:54:56","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ams\/?post_type=tribe_events&p=44528"},"modified":"2023-04-04T10:58:52","modified_gmt":"2023-04-04T14:58:52","slug":"ams-weekly-seminar-murray-b-sachs-professor-natalia-trayanova","status":"publish","type":"tribe_events","link":"https:\/\/engineering.jhu.edu\/ams\/event\/ams-weekly-seminar-murray-b-sachs-professor-natalia-trayanova\/","title":{"rendered":"AMS Weekly Seminar | Murray B. Sachs Professor Natalia Trayanova"},"content":{"rendered":"
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Location:\u00a0<\/span><\/strong>Gilman 132<\/span><\/p>\n<\/div>\n

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When: <\/span><\/strong>April 6th\u00a0at 1:30 p.m.<\/span><\/p>\n<\/div>\n

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Title:\u00a0<\/span><\/strong>AI-Powered Personalized\u00a0Computational\u00a0Cardiology<\/p>\n<\/div>\n

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Abstract:\u00a0<\/span><\/strong>Precision medicine is envisioned to provide therapy tailored to each patient. The rapidly increasing ability to capture extensive patient data, coupled with machine learning, is a pathway to achieving this vision. A different pathway towards precision medicine is the increasing ability to encode known physics laws and physiology knowledge within mathematical equations and to adapt such models to represent the behavior of a specific patient.<\/p>\n

Wouldn\u2019t it be great to have a digital representation of ourselves that allows doctors to simulate our personal medical history and health conditions using relationships learned both from data and from biophysics knowledge? That virtual replica of ourselves would integrate data-driven machine learning and multiscale physics-based modeling to continuously update itself as our health condition changes and more information about our interaction with the environment is acquired. These digital twins<\/i> would forecast the trajectory of the patient\u2019s disease, estimate risk of adverse events, and predict treatment response so that the potential outcome would inform treatment decision.<\/p>\n

This presentation explores the synergies that have been achieved between machine learning and mechanistic physics-based heart models towards enabling precision medicine in cardiology. It showcases how machine learning and multiscale cardiac modeling complement each other in engineering your heart\u2019s health. A highlight is the robust prediction of sudden cardiac death risk in different heart diseases. Another application of the heart digital twin technology is illustrated by the development of a precise treatment for patients suffering from arrhythmias. This application prevents future re-hospitalizations and repeat procedures, shifting the treatment selection from being based on the state of the patient today to optimizing the state of the patient tomorrow.<\/p>\n<\/div>\n

Zoom link:<\/strong> https:\/\/wse.zoom.us\/j\/95738965246<\/p>\n

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 <\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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