● Analysis of medical and big data.
● Use of machine learning algorithms to assess and improve the health and safety of staff in hospitals and clinics.
● Electronic Health Record (EHR) and e-Prescribing.
● Robotic surgery – Da Vinci system.
● Nuclear magnetic resonance.
● Electromagnetic dosimetry for mobile communication devices.
● Virtual simulation of radiotherapy treatment – Galinos simulation software.
● DICOM image analysis and processing using Python.
- Teacher: Δημήτρης Κουτσούρης
- Teacher: Κωνσταντίνα Νικήτα
ECTS : 6
Study Load : theory 1, lab 3
Language : el
Learning Outcomes : Upon successful completion of the course, students will be able to:
● Understand the nature and key characteristics of large-scale data in healthcare and apply basic techniques for modelling, analysis, interpretation and information extraction.
● Know and leverage knowledge-discovery techniques (machine learning algorithms) to address health and safety issues for staff in hospitals.
● Understand and identify the functional and technical characteristics of the Electronic Health Record and evaluate existing standard models and clinical coding systems (and their role) in managing issues related to safety and interoperability.
● Understand the operating principles, architecture and technical features of robotic systems in modern surgery.
● Understand core concepts of nuclear magnetic resonance (free induction decay, spin-echo signal, gradient field) and use them to distinguish biological tissues.
● Know how to select timing parameters of NMR pulse sequences for measuring the longitudinal (T1) and transverse (T2) relaxation times of biological tissues.
● Design coils for generating the magnetic fields used in magnetic resonance imaging.
● Understand the thermal effects of radio-frequency electromagnetic fields on biological tissues and know the dosimetric quantities used in international regulations limiting human exposure to electromagnetic radiation.
● Assess the thermal effects of interactions between electromagnetic radiation sources and biological tissues by designing and conducting appropriate computational and experimental simulations.
● Produce measurable results that can be used in studies of electromagnetic dosimetry.
● Know the architecture and main functional characteristics of a radiotherapy treatment simulator and design the conceptual framework of analogous decision-support systems for therapy.
● Apply techniques for analysing and processing medical images stored using the DICOM standard, and understand the basic building blocks of digital images and their processing techniques.
Through successful participation in the course, students will further develop:
● The ability to work both independently and as part of a team.
● The ability to collaborate effectively and make appropriate decisions towards achieving a common learning goal (preparation of a group deliverable/semester project).
● Communication skills and confidence through teamwork and the presentation of scientific ideas and results to an audience.
● Critical thinking through the analysis, synthesis and solution of case studies related to state-of-the-art biomedical applications.
● The ability to deepen and broaden their knowledge base within the interdisciplinary field of Biomedical Engineering.
● The ability to search for, analyse and synthesise data and information using modern technologies and tools/software.