BME Distinguished Speaker Series - Elena Di Martino
- Dec. 6, 2024 12:00 PM - 1:00 PM
- Donadeo ICE, 8-207
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The Biomedical Engineering Distinguished Speaker Series is targeted to all those across campus (faculty, students, alumni) interested in the application of science and engineering to medicine.
Elena Di Martino is a Professor in the Department of Biomedical Engineering at the University of Calgary, where she has been a faculty member since 2007. She earned her PhD from a joint program between the University of Milano and Politecnico of Milano, specializing in engineering and biomedical applications.
Di Martino is renowned for her work in cardiovascular biomechanics and developing image-guided tools for aortic aneurysm therapy. Her research has led to two awarded patents and eight pending, focusing on tissue mapping, repair methods, and machine learning.
Leading the Biological Tissue Mechanics Lab, Di Martino and her team are at the forefront of developing advanced methodologies to assess the performance of endovascular prostheses, which are utilized in non-invasive aneurysm repairs. Her research efforts also extend to establishing protocols that connect image-based indices with tissue-level weakening in the arterial wall.
In 2020, she co-founded ViTAA Medical Solutions, an imaging software company that creates AI tools to help surgeons optimize treatment for abdominal aortic aneurysms.
Abstract
“Towards better assessment tools for aortic aneurysms”
Patients diagnosed with aortic aneurysm disease are typically triaged for intervention primarily based on the maximum diameter of the aortic sac. This approach often neglects the significant influence of body habitus, patient-specific risk factors, and tissue-related factors on the risk of rupture. Such a "one-size-fits-all" methodology in surgical triage has demonstrated a disappointing track record, potentially resulting in premature ruptures or unnecessary surgical interventions.
Recently, we proposed a biomechanics index to assess wall weakening in aortic aneurysms, which is founded on parameters such as wall deformability, abnormal flow, and the extent of intraluminal thrombus coverage. This index has been experimentally validated using surgical specimens. Subsequently, artificial intelligence (AI) was employed to examine the correlation between localized tissue weakness and rapid aneurysm growth, which serves as a predictor of adverse clinical outcomes.
Furthermore, we classified aneurysms based on the behaviour of the intraluminal thrombus, as evaluated exclusively through multiphase computed tomography (CT) imaging. This classification system has the potential to enhance the assessment of clinical risk associated with aneurysms by identifying specific features that promote rapid thrombus growth and may be associated with mechanical failure.
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