MSc Biomedical Engineering and Technology | 2nd semester

MSc Biomedical Engineering and Technology

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2^nd Semester

BMET.201: Diagnostic Medical Imaging Systems (Required)

Course code:	BMET.201
Course title:	Diagnostic Medical Imaging Systems (Required)
Coordinator:	Stratos David, Assistant Professor, Department of Biomedical Engineering, University of West Attica, Greece sdavid@uniwa.gr
Co-teachers:	Costas Arvanitis, Assistant Professor, Georgia Institute of Technology, George W. Woodruff School of Mechanical Engineering, USA costas.arvanitis@gatech.edu Panagiotis Liaparinos, Associate Professor, Department of Biomedical Engineering, University of West Attica, Greece liapkin@uniwa.grGeorge Fountos, Professor, Department of Biomedical Engineering, University of West Attica, Greece gfoun@uniwa.gr Ioannis Kandarakis, Professor Emeritus, Department of Biomedical Engineering, University of West Attica, Greece kandarakis@uniwa.gr
Teaching method:	Intensive, within 3-4 weeks (lectures + on-site visits + project) Exams: Quiz paper + project assignment + project presentation
Course contents:	Fundamental concepts in Image Science and Nuclear Medicine Interactions of high energy photons and particles with matter X-ray Tubes, Radioactivity and modes of radioactive decay Basic structure of X-ray energy integrated detectors and gamma photon counting detectors Imaging Instrumentation of Radiology imaging systems Imaging Instrumentation of Nuclear Medicine imaging systems Image Reconstruction Techniques Image Quality and Quality Control Clinical Medical imaging examples (including planar, SPECT, PET etc) Practical exercises in a gamma spectroscopy
Learning outcomes:	Upon completion of this course, students will have a strong foundation in diagnostic imaging systems and techniques. They will possess the knowledge and skills necessary to investigate, and interpret images from various diagnostic imaging modalities. Moreover, this module includes assignments. Upon completion of the assignments, students will demonstrate their ability to apply their knowledge and skills, think critically, communicate effectively, and work collaboratively. They will enhance their problem-solving abilities, develop effective communication skills, and improve technical writing and oral presentations. Finally, this module includes field visits to research centres. Upon completion of field visits to research centres, students will have gained exposure to advanced research, practical skills, and professional connections. They will be better prepared to apply their academic knowledge in real-world contexts, make informed career choices, and understand the impact of their field of study in research.
ECTS:	5
Semester:	2^nd

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BMET.202: Biomedical Instrumentation (Required)

Course code:	BMET.202
Course title:	Biomedical Instrumentation (Required)
Coordinator:	Dimitris Glotsos, Professor, Department of Biomedical Engineering, University of West Attica, Greece dimglo@uniwa.gr
Co-teachers:	Erricos Ventouras, Professor, Department of Biomedical Engineering, University of West Attica, Greece ericvent@uniwa.gr Klaus Peter Koch, Hochschule Trier, Germany koch@hochscule-trier.de
Teaching method:	Intensive, within 3-4 weeks (lectures + on-site visits + project) Exams: Quiz paper + project assignment + project presentation
Course contents:	Introduction to Biomedical Instrumentation Bio-Sensors, Bio-Signals, biomedical electronics, amplifiers, filters, signal conditioning, microcontrollers, microprocessors Neuroengineering and implants In vitro diagnostics instrumentation Medical imaging instrumentation Vital signs monitoring instrumentation (temperature, pressure, flow, ECG, EEG, EMG) Patient safety
Learning outcomes:	Upon completion of this course, students will have a solid foundation in biomedical instrumentation principles, techniques, and applications. They will possess the knowledge and skills necessary to design, develop, and evaluate biomedical instruments. Moreover, this module includes assignments. Upon completion of the assignments, students will demonstrate their ability to apply their knowledge and skills, think critically, communicate effectively, and work collaboratively. They will enhance their problem-solving abilities, develop effective communication skills, and improve technical writing and oral presentations. Finally, this module includes hands-on lab exercises. Upon completion of hands-on labs, students will have gained practical skills, applied theoretical knowledge, developed problem-solving abilities, and enhanced their ability to work collaboratively. They will be better prepared to bridge the gap between theory and practice, successfully engage in scientific experimentation, and apply their skills in various academic and professional settings.
ECTS:	5
Semester:	2^nd

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BMET.203: The Biomedical engineering industry sector ΙI (Required)

BMET.204: Emergency medicine (Elective)

BMET.205: Control systems in biomedical engineering (Elective)

Course code:	BMET.205
Course title:	Control systems in biomedical engineering (Elective)
Coordinator:	Irina Andra Tache, Assistant Professor, Faculty of Automatic Control and Computers, Politehnica University of Bucharest, Romania irina.andra@gmail.com
Co-teachers:	–
Teaching method:	Intensive, within 3 weeks (lectures + on-site visits + project) Exams: Quiz paper + project assignment + project presentation
Course contents:	The basis of control systems Mathematical modelling of biophysical systems Control systems at human body level (thermoregulation, osmoregulation, blood glucose regulation) Numerical control systems and hardware implementation in Arduino Applications of control systems in biomedical process (artificial pancreas, artificial heart)
Learning outcomes:	Upon completion of this course, students will possess the knowledge and skills necessary to design, analyse, and implement control systems in biomedical engineering. They will be prepared to work in research, development, and clinical settings where control systems are utilized for improving healthcare outcomes, developing medical devices, and advancing the field of biomedical engineering. Moreover, this module includes assignments. Upon completion of the assignments, students will demonstrate their ability to apply their knowledge and skills, think critically, communicate effectively, and work collaboratively. They will enhance their problem-solving abilities, develop effective communication skills, and improve technical writing and oral presentations. Finally, this module includes hands-on lab exercises. Upon completion of hands-on labs, students will have gained practical skills, applied theoretical knowledge, developed problem-solving abilities, and enhanced their ability to work collaboratively. They will be better prepared to bridge the gap between theory and practice, successfully engage in scientific experimentation, and apply their skills in various academic and professional settings.
ECTS:	5
Semester:	2^nd

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BMET.206: Bioinformatics (Elective)

BMET.207: Human machine interaction in healthcare (Elective)

Course code:	BMET.207
Course title:	Human machine interaction in healthcare (Elective)
Coordinator:	Luis Pinto Coelho, Professor, Instituto Superior De Engenharia do Porto (ISEP), Politécnico do Porto, Portugal lfc@isep.ipp.pt
Co-teachers:	Luis Pinto Coelho, Professor, Instituto Superior De Engenharia do Porto (ISEP), Politécnico do Porto, Portugal lnm@isep.ipp.pt
Teaching method:	Intensive, within 3 weeks (lectures + on-site visits + project) Exams: Quiz paper + project assignment + project presentation
Course contents:	1. Introduction (- General topics on Human-Machine Interaction in Healthcare, – General topics about image and signal processing in healthcare, – Examples and practical exercises covering image and signal pre-processing) 2. Computer assisted detection of non-pathological conditions (- Emotional state, – Pain, – Airway obstruction, – Snoring and sleep quality, – Applications, – Examples and applications) 3. Computer assisted detection of pathological conditions (- Parkinson, Alzheimer, Schizophrenia, – Depression, – COVID-19, – Examples and applications) 4. Designing and Evaluating Human-Machine interfaces
Learning outcomes:	Upon completion of this course, students will possess the knowledge and skills necessary to design, evaluate, and implement human-machine interaction solutions in healthcare services. They will understand how to develop user-friendly and effective technology interfaces that enhance healthcare delivery and improve patient outcomes. Moreover, this module includes assignments. Upon completion of the assignments, students will demonstrate their ability to apply their knowledge and skills, think critically, communicate effectively, and work collaboratively. They will enhance their problem-solving abilities, develop effective communication skills, and improve technical writing and oral presentations. Finally, this module includes hands-on lab exercises. Upon completion of hands-on labs, students will have gained practical skills, applied theoretical knowledge, developed problem-solving abilities, and enhanced their ability to work collaboratively. They will be better prepared to bridge the gap between theory and practice, successfully engage in scientific experimentation, and apply their skills in various academic and professional settings.
ECTS:	5
Semester:	2^nd

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BMET.208: Machine Learning in Medicine and Biology (Elective)

Course code:	BMET.208
Course title:	Machine Learning in Medicine and Biology (Elective)
Coordinator:	Dionisis Cavouras, Professor Emeritus, Department of Biomedical Engineering, University of West Attica, Greece cavouras@uniwa.gr
Co-teachers:	Cristina Soguero Ruiz, Assistant Professor, Dpto. Teoría de la Señal y Comunicaciones y Sistemas, Universidad Rey Juan Carlos, Spain cristina.soguero@urjc.es
Teaching method:	Intensive, within 3 weeks (lectures + on-site visits + project) Exams: Quiz paper + project assignment + project presentation
Course contents:	Introduction Machine Learning in Medicine and Biology (BioMed_ML) Statistics, Biomedical Data Bases, and Software Tools Used in BioMed_ML Supervised BioMed_ML Unsupervised BioMed_ML Deep and Reinforcement Learning in Medicine and Biology Implementation of Machine Learning Models
Learning outcomes:	Upon completion of this course, students will possess the knowledge and skills necessary to apply machine learning techniques to medical and biological data analysis. They will be prepared to work in research institutions, healthcare organizations, and biomedical engineering companies, where they can contribute to advancing precision medicine, improving diagnostics, and gaining insights from complex biomedical datasets. Moreover, this module includes assignments. Upon completion of the assignments, students will demonstrate their ability to apply their knowledge and skills, think critically, communicate effectively, and work collaboratively. They will enhance their problem-solving abilities, develop effective communication skills, and improve technical writing and oral presentations. Finally, this module includes hands-on lab exercises. Upon completion of hands-on labs, students will have gained practical skills, applied theoretical knowledge, developed problem-solving abilities, and enhanced their ability to work collaboratively. They will be better prepared to bridge the gap between theory and practice, successfully engage in scientific experimentation, and apply their skills in various academic and professional settings.
ECTS:	5
Semester:	2^nd

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BMET.209: Science, Technology, Society: Biomedical Engineering, Social Aspects, Ethics (Elective)

Course code:	BMET.209
Course title:	Science, Technology, Society: Biomedical Engineering, Social Aspects, Ethics (Elective)
Coordinator:	Katerina Vlantoni, Postdoctoral Researcher and Principal Investigator, BIO-CONTEXT H.F.R.I. Research Project, Department of History and Philosophy of Science, National and Kapodistrian University of Athens avlantoni@uniwa.gr
Guest lecturer:	Aristotle Tympas, Professor, Department of History and Philosophy of Science, National and Kapodistrian University of Athens tympas@phs.uoa.gr
Co-teachers:	Ioannis Kandarakis, Professor Emeritus, Department of Biomedical Engineering, University of West Attica, Greece kandarakis@uniwa.gr
Teaching method:	Intensive, within 3 weeks (lectures + on-site visits + project) Exams: Quiz paper + project assignment + project presentation
Course contents:	History of biomedical engineering The institutional basis of biomedical engineering: funding, professional and research bodies and societies Medical diagnostic technologies: emergence and use Biomedical technologies, health systems and society Biomedical informatics, Artificial intelligence, Big Data, Algorithms: historical and contemporary issues Ethics of biomedical engineering
Learning outcomes:	Upon completion of this course, students will possess the knowledge and skills necessary to critically analyse the social, ethical, and cultural dimensions of biomedical engineering. They will be equipped to navigate complex ethical challenges, and contribute to the responsible and socially conscious advancement of biomedical engineering in alignment with societal values and needs. Moreover, this module includes assignments. Upon completion of the assignments, students will demonstrate their ability to apply their knowledge and skills, think critically, communicate effectively, and work collaboratively. They will enhance their problem-solving abilities, develop effective communication skills, and improve technical writing and oral presentations.
ECTS:	5
Semester:	2^nd

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For the successful completion of the MSc program a minimum of ninety (90) ECTS is required, with at least 30 ECTS per semester.

For the 1^st and 2^nd semester, students should successfully complete all Required courses of each semester (Required courses are assigned with 15 ECTS) and select at least three Elective courses (Elective courses are assigned with 5 ECTS each).

For the 3^rd semester, students should successfully complete the Diploma thesis (Diploma thesis is assigned with 30 ECTS). Guidelines for Diploma Thesis can be found here.

The detailed examination regulation may be found at the Program structure and Internal Regulation document.