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Detail

Francesco Moscato
Univ.-Prof. Dipl.-Ing. Francesco Moscato, PhD

Center for Medical Physics and Biomedical Engineering
Position: Professor

ORCID: 0000-0003-0279-6615
T +43 1 40400 39830
francesco.moscato@meduniwien.ac.at

Keywords

Artificial Intelligence; Artificial Organs; Biomedical Engineering; Cardiovascular System; Medical 3D-Printing; Models, Cardiovascular; Pattern Recognition, Automated; Signal Processing, Computer-Assisted

Research group(s)

Research interests

My research focuses on two main areas: medical 3d-printing and cardiovascular bioengineering. In particular, my research addresses the investigation of how 3d-printing and advanced AI-powered design methods can improve surgical and interventional procedures, medical device prototyping, tissue engineering and medical education. Furthermore, my efforts are devoted towards research and development of methods and devices to improve diagnostics and provide support to a range of cardiovascular pathologies, utilizing wearable and implantable sensing technologies and advanced artificial intelligence algorithms.

Techniques, methods & infrastructure

Additive manufacturing (aka 3d-printing) and digital 3D technologies: Fused filament fabrication, stereolithography, multimaterial ink-Jetting, selective laser metal melting, lithography-based ceramic manufacturing, 2-photon polymerization, extrusion bio-printing, field-driven generative design, topology optimization, augmented reality incl. real-time finite element simulations. Mechanical and hemodynamic in-vitro and ex-vivo test setups, mathematical modeling including computational fluid dynamics, system identification and control, biosignal processing/machine learning using deep neural networks (e.g. CNN, RNN, BiLSTM, Transformers, GANs) and explainable-AI methods.

Grants

Selected publications

  1. Civilla, L. et al. (2024) ‘Development and assessment of case-specific physical and augmented reality simulators for intracranial aneurysm clipping’, 3D Printing in Medicine, 10(1). Available at: https://doi.org/10.1186/s41205-024-00235-w.
  2. Haberbusch, M. et al. (2024) ‘Decoding cardiac reinnervation from cardiac autonomic markers: A mathematical model approach’, The Journal of Heart and Lung Transplantation, 43(6), pp. 985–995. Available at: https://doi.org/10.1016/j.healun.2024.01.018.
  3. Vostatek, M. et al. (2024) ‘Bone‐Mimetic Osteon Microtopographies on Poly‐ε‐Caprolactone Enhance the Osteogenic Potential of Human Mesenchymal Stem Cells’, Macromolecular Bioscience [Preprint]. Available at: https://doi.org/10.1002/mabi.202400311.
  4. Moscato, F. et al. (2021) ‘The left ventricular assist device as a patient monitoring system’, Annals of Cardiothoracic Surgery, 10(2), pp. 221–232. Available at: https://doi.org/10.21037/acs-2020-cfmcs-218.
  5. Moscato, F. et al. (2013) ‘Use of continuous flow ventricular assist devices in patients with heart failure and a normal ejection fraction: A computer-simulation study’, The Journal of Thoracic and Cardiovascular Surgery, 145(5), pp. 1352–1358. Available at: https://doi.org/10.1016/j.jtcvs.2012.06.057.