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The Critical Role of Computational Modeling in Future Diagnostic, Monitoring, and Predictive Tools for Cardiovascular Diseases
November 29 @ 4:30 pm - 5:30 pm
Join the IEEE Toronto Instrumentation & Measurement – Robotics & Automation Joint Chapter for a talk on The Role of Computational Modeling in Future Diagnostic, Monitoring, and Predictive Tools for Cardiovascular Diseases , presented by Dr. Zahra K. Motamed. Tuesday, November 29, 2022 @ 4:30 – 5:30 PM Abstract: The main functions of the cardiovascular system are to transport, control and maintain blood flow in the entire body. Abnormal hemodynamics greatly alters this tranquil picture, leading to initiation and progression of disease. These abnormalities are often manifested by disturbed fluid dynamics (local hemodynamics), and in many cases by an increase in the heart workload (global hemodynamics). Hemodynamics quantification can be greatly useful for accurate and early diagnosis, but we still lack proper diagnostic methods for many cardiovascular diseases because the hemodynamics analysis methods that can be used as engines of new diagnostic tools are not well developed yet. Furthermore, as most interventions intend to recover the healthy condition, the ability to monitor and predict hemodynamics following particular interventions can have significant impacts on saving lives. Despite remarkable advances in medical imaging, imaging on its own is not predictive. Predictive methods are rare. They are extensions of diagnostic methods, enabling prediction of effects of interventions, allowing timely and personalized interventions, and helping critical clinical decision making about life-threatening risks based on quantitative data. Dr. Motamed and her team has developed innovative non-invasive image-based patient-specific diagnostic, monitoring and predictive computational-mechanics framework for patients with cardiovascular disease. Currently, none of the above metrics can be obtained noninvasively in patients in clinics and when invasive procedures are undertaken, the collected metrics cannot be by any means as complete as the results that Motamed lab’s framework provides. Speaker(s): Zahra K. Motamed, PhD , Virtual: https://events.vtools.ieee.org/m/330836