Curriculum and Courses

Fundamental principles of molecular cellular biology and human physiology, including the building blocks of life, evolution of life on earth, methods in molecular biology, genomic organisation, flow of genomic information, cell and tissue types, human organ systems and homeostasis.

The module focuses on the main elements of gross and functional anatomy of human body. Additionally, it offers strong stimuli to postgraduate students by integrating in its curriculum specific medico-engineering applications related to human anatomy.

Basic principles of biomedical signal processing and analysis. Origin, nature and types of signals produced by the brain and nervous system. Noise detection and removal, segmentation, feature extraction for clinical diagnosis and classification

This course introduces fluid and solid mechanics of the human cardiovascular system and their application towards the development of technology for cardiovascular disease diagnosis and treatment.  

Fundamental principles of laser physics and their applications in medicine, including laser-tissue interactions, optical properties of biological tissues, and clinical uses of medical lasers.

In the Biomaterials part, the course aims to introduce the class of materials of biological origin, their molecular structure and architecture, the mechanisms of self-organization. It also aims to introduce all classes of synthetic biomaterials and their basic mechanisms of action. In the Biofabrication part the course focuseson specific technologies used to create a biofabricated constructs.

The course introduces the basic concepts of Tissue Engineering and Regenerative Medicine (TERM) with applications in bone, cartilage, cardiovascular and neural tissue. The students will be exposed to the wide variety of tissue engineering applications and stem cell therapies, assessment of biocompatibility, cell-biomaterial interactions and biomechanics.

This course introducesthe basic concepts of Molecular Pharmacology required for the design, development and evaluation of novel drugs, the structural-functional characterization of receptors and the design of novel molecules. In addition, it focuses on the basic principles of material science for the development of functional biomaterials, theranostic agents, novel applications of stem cells as therapeutics against neurodegenerative diseases and translational regenerative pharmacology.

This course introducesmaterials, microfabrication and design concepts of BioMEMS (including lab-on-chip systems, microfluidics, biosensors and actuators), a wide variety of applications of BioMEMS and biosensors in the field of life sciences and agro/food safety, biomedical devices and platforms for molecular diagnostics. In microfluidic applications, from single cells to model organisms,the topics in biology research includeapplications in studying development, controlling cell size/shape, multiplexed and high-throughput phenotyping, spatio/temporal signaling, bacteria and whole organism sensing, organ on-a-chip.

This course introduces the basics of -omics technologies (genomics, transcriptomics, proteomics, metabolomics, radiomics). Gene modulation technologies (for example gene therapy, CRISPR-Cas9) in disease treatment will be also described in the context of precision medicine and molecular diagnostics in Haematology and Oncology, and Omics applications on cellular therapies. Effective implementation of novel technologies and precision medicine approaches in clinical practice will be described, including the use of radiomics and their integration with other omics types.

Basic physics/principles and instrumentation of diagnostic X-ray and computed tomography imaging, nuclear medicine procedures for diagnosis/treatment and radiotherapy procedures. Radiation protection basics. Clinical applications.

Lens optics and fiber optics, semiconductor physics, imaging sensor physics and engineering, spectral and hyperspectral imaging, rigid and flexible endoscopes and clinical microscopes, endoscope system design considerations, contrast agents and molecular probes in endoscopy, advanced multimodal endoscopy, performance validation-modulation transfer function, clinical practices and examples, industry review.

Introduction to biomedical imaging technologies and modern microscopy techniques. Basic concepts of different microscopy methods and their application to visualize biological processes. Simple image processing tools and processes.

Βasic principles of medical image analysis focusing on both classical approaches for image segmentation, registration, quantification, texture analysis and filtering, as well as on AI approaches, including radiomics and deep learning, for diagnostic/prognostic model development.

The course will introduce the students to the basic concepts of Artificial Intelligence (AI) and Machine Learning (ML); will present to the students state-of-the-art applications of AI and ML in medicine and healthcare, focusing on decision-support systems for disease diagnosis and treatment; will develop the students’ interest for such systems, and for AI and ML in general.What is AI; History of AI; Symbolic AI and Expert Systems; Statistical AI. Data-driven AI; The Intelligent Agent paradigm; AI for medicine: an overview; Intelligent Search methods. Informed Search and Uninformed Search; Constraint Satisfaction Problems; Applications in the Medical Domain; Logic: propositional logic and first-order logic; Logical Inference; Decision-making under uncertainty: Bayesian reasoning, Markov Decision Processes; Expert Systems for Medical Decision Support and Assistive Technologies (symbolic AI-based expert systems, Bayesian systems, hybrid approaches); Machine Learning: Neural Networks, Deep Learning, and their applications in medicine and healthcare.

Modern biology, both molecular and evolutionary, is virtually impossible without computational methods. The amount of biological data, obtained from re-sequencing projects, genomics, gene expression, or phylogenetics require specialized software for data handling and analysis. Students will learn the basics of Python programming; be able to handle modern datasets; perform common statistical analyses such as hypothesis testing, detection of differential expressed genes etc.; be able to use databases such as Gene Expression Omnibus (GEO) to download publicly available datasets; basic usage of command line and terminal in Linux; acquire knowledge of specific dataset such as Next Generation Sequencing Datasets and get familiar with basic tools used to analyze them. Introduction to biology for bioinformatics; General presentation of R programming language and R-studio; Microarray datasets; Data analysis for microarray datasets; NGS datasets; Linux in Bioinformatics

The application of information and communications technology (ICT) in health care has grown exponentially over the last 30 years and there's strong evidence about its potential to improve effectiveness and efficiency in the medical domain. In this course we therefore overview the ICT landscape in health care and provide examples of these technologies for medical, mobile, and remote health applications. The objective of the course is to give students an introduction to the health care informatics, the systems and the interoperability standards for implementing biomedical applications. Introduction to Health care Information: Clinical, Administrative, Patient specific, Aggregate; Patient Records: Electronic Medical Records (EMRs), Electronic Health Records (EHRs), Personal Health Records (PHRs); ICT in Healthcare and standardization: PACS-Based Multimedia Imaging Informatics, Medical Imaging: the DICOM standard; Vocabularies, Thesauri, and Healthcare Terminology Standards: SNOMED-CT, ATC, LOINC, ICD; Messaging and Document Exchange: HL7v2, CDA, and HL7 FHIR; Healthcare interoperability: IHE integration profiles; Advanced and emerging use of health information technology: Mobile Health (mHealth), Internet of Medical Things (IOMT), Information exchange across borders, Security, privacy, and confidentiality.

The course will familiarize students with the AI and robotic technologies that revolutionize the field of healthcare. Students will learn the latest in emerging technologies for minimal invasive surgery, telesurgery, telementoring and telemedicine; will introduce the students in the basic principles of Robotic Assistive Surgery, teleoperation, and VR medical training systems; will develop students’ interest in the latest emerging technologies of AI and automation for medical provision.Introduction to Medical Robotics; VR, telementoring and remote training; Medical Modeling, Simulation & Visualization (MMSV); Structure and function of medical robots; Robotic Assistive Minimally Invasive Surgery (MIS); Haptic Feedback and Visual Servoing; Perceptual Docking in MIS; A.I. for Telemedicine.

The course will introduce the students to the basic concepts of big data analytics in medicine and health care. Students will learn how to collect, store and transform heterogeneous healthcare data, exploiting medical ontologies and terminologies for semantically uplifting them. Then they will learn how to efficiently and effectively process them through big data frameworks, enabling predictive modeling and computational phenotyping. In addition they will understand how graph analytics can be used to visualize and interpret data and effectively communicate results and findings. The course will familiarize students with the current procedures and requirements for processing big healthcare data and for the available technologies; will develop the students’ interest in personalized medicine through big data analytics and to help them understand the challenges and the opportunities in the area.Intro of Big Data Analytics in Medicine and Health Care/Course overview; Collecting, Storing and Transforming Healthcare Data; Medical Ontologies & Terminologies; Big Data Frameworks; Predictive modeling; Computational Phenotyping; Graph Analytics and Visualization; Opportunities and Challenges in Health Data analytics.