Simona Turco is an assistant professor at the Signal Processing Systems group (department of Electrical Engineering), and member of the Biomdical Diagnostics (BM/d) research lab and of the Eindhoven MedTech Innovation Center (e/MTIC). Her research focuses on quantitative model-driven analysis of medical images and biosignals, with focus on oncology and perioperative care. As cancer growth and aggressiveness relate to angiogenesis, i.e., the formation of chaotic network of leaky microvessels, her research in cancer diagnostics is devoted to developing novel imaging solutions for assessment of angiogenesis. To go beyond structural imaging, ultrasound and MRI dynamic imaging with injection of contrast agents are used to extract structural, functional, and molecular information on angiogenic vasculature at different spatial and temporal scales. In the context of perioperative care, Turco's research focuses on model-based analysis of biosignals for accurate prognosis and timely identification of patients at risk. By combining model-based feature extraction with data-driven approaches for optimal feature selection, she aims at developing effective risk prediction models to support clinical decision making. In general, Turco aims at facilitating the translation of research, driven by industrial and clinical needs, through tight collaborations with top healthcare partners.
If a picture is worth a thousand words, what is a model worth?
Simona Turco obtained her MSc in Biomedical Engineering from the University of Pisa (Italy) in 2012 summa cum laude. In 2015, she obtained a professional doctorate in engineering (PDEng) in Healthcare system design from the Stan Ackermans Institute at University of Technology (TU/e). Here, she also completed her Phd in 2018, with thesis entitled “Pharmacokinetic modeling in cancer: from functional to molecular imaging of angiogenesis”. Currently, she is assistant professor at the Biomedical Diagnostics (BM/d) research lab, focusing on quantitative model-driven analysis of bio-signals.
Automated detection and classification of patient–ventilator asynchrony by means of machine learning and simulated dataComputer Methods and Programs in Biomedicine (2023)
New Hemodynamic Parameters in Peri-Operative and Critical CareSensors (2023)
Evaluation of the accuracy of established patient inspiratory effort estimation methods during mechanical support ventilationHeliyon (2023)
Pharmacokinetic modeling of the Second-wave Phenomenon in Nanobubble-based Contrast-enhanced UltrasoundIEEE Transactions on Biomedical Engineering (2023)
A model-based approach to generating annotated pressure support waveformsJournal of Clinical Monitoring and Computing (2022)
Current Educational Activities
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