MedTech - Learning outcomes


Mathematics (MAT/08)

•    Basics of numerical approximation 
•    Mathematical basics of Artificial Intelligence 
•    Mathematics of imaging 
•    Mathematics of neuroscience 
•    Mathematics of genomics

Basic of computer science (INF/01)

•    Understand the rules and syntax used in programming languages 
•    Identify and analyze a problem and select the appropriate programmatic methods to solve that problem 
•    Have familiarity with programming constructs 
•    Have a systematic understanding of how to run and create a Python program 
•    acquire the ability to write and tune a program that automatizes simple computational tasks 
•    Write functional code in Python 
•    Analyse and visualize data by using Python's standard and third-party libraries.

Biomechanical analysis and simulation of human movement (ING-INF/06)

•    Describe kinematic and kinetic movement pattern 
•    Use available devices for kinematic and kinetic analysis 
•    Execute experimental activity in a movement analysis lab. 
•    Use of open-source software for kinematic and kinetic analysis 
•    Use of open-source software for modelling muscolo-skeletal structures and dynamical simulation of movement

Biomaterials (ING-IND/34)

The teaching provides students with basic knowledge on biomaterials research and development including discussion on different types of materials used for biomedical applications and their relevant properties 
The objectives are: 
•    to gain knowledge on main classes of materials used in biomedical applications, namely metals, ceramics, polymers, and hydrogels 
•    to learn basic principles of surface engineering 
•    to understand the concept of biocompatibility applied to biomaterials and to gain knowledge on its evaluation 
•    to gain knowledge on basic principles of materials for nanomedicine.

Bioelectronics (ING-INF/06)

•    Describe the phenomena of electrical conduction in electronic systems and devices and in simple biophysical structures with particular emphasis on cell membranes 
•    Describe simple circuit models of electronic components and biophysical structures 
•    Solve simple application problems related to basic electronic circuits and simple biophysical systems


Biostatistics (MED/01)

The course of Biostatistics will give the students the instruments to perform a statistical analysis of data: the main topics covered will be the following: 
•    Principles of statistical inference and the meaning of p value. 
•    The concept of confounding and the methods to adjust for it: the multivariate analysis. 
•    Methods of statistical analysis of data: linear regression, logistic regression, survival analysis, Poisson and Negative Binomial regression. 
•    Visualization of data

Medical Informatics (ING-INF/06)

•    To define simple clinical data model 
•    To pass from Excel tables to real structured data collection systems 
•    To understand the use of standards in medical informatics

Analysis of biomedical signals (ING-INF/06)

In this course we will cover the basic concepts behind signal processing such as Fourier theorem and filter design. At the end, the student will be able to: 
•    Manage pre-processing steps of time-varying signals 
•    Design simple filters e.g., frequency selection or noise removal filters 
•    Perform time-frequency signal transformation

Biomedical instrumentation (ING-IND/34)

At the end of the course the student will be able to 
•    Interpret and represent the result of a measurement 
•    interpret the technical specifications of a biomedical instrument and evaluate its performance within a given application scenario 
•    describe the working principles of the most common strain, pressure, displacement, temperature transducers used in biomedical instrumentation 
•    describe the analog to digital and digital to analog conversion principles and their implications in instrument performance

Biomedical image processing (MED/36)

•    The course aims to provide basic knowledge about image reconstruction and analysis of digital images obtained by both radiological and nuclear medicine methods. 
•    Procedures to obtain quantitative estimations of structural and physiological parameters will be provided for both planar and tomographic images 
•    Procedures for construction of parametric maps of physiological variables will be also provided using compartmental analysis tool or harmonic fitting.



Fundamentals of Machine Learning in medicine (INF/01)

The course aims at providing basic knowledge of Machine Learning methods applied in the biomedical context. We expect that the students will learn how to: 
•    Confidently read state-of-the-art publications in the context of Machine Learning used in medicine 
•    Identify and define different types of Machine Learning tools and techniques 
•    Know the limits of ML techniques and be aware of reproducibility issues 
•    Discuss how to apply Machine Learning to one’s own medical learning and training 
•    Understand how data-driven decisions are made and assessed 
•    Actively participate in the selection, purchase, and deployment of Machine Learning based medical software. 
•    Search datasets among those available in the public domain, suitably preprocess data and set up a simple Machine Learning model

Technoetics (M-FIL/03)

The first part of the course will address the relationship between science, technology, and values trying to answer questions such as: Which values are involved in biomedical sciences? Which is their role? When and how are they legitimate? The second part of the course will address ethical issues posed by scientific and technological advances, explore related moral dilemmas, and offer theoretical and practical ways to deal with them.

Track1: Bioengineering for personalize medicine

Bioinformatics and proteomics (INF/01)

Students will learn basic elements in pipeline of high-throughput data analysis: crash course on molecular biology; overview on sequencing technologies; alignment and normalization algorithms; QC criteria unsupervised and supervised learning methods for subtyping and data exploration as well as variable selection and functional characterization; network reconstruction algorithms.

Tissue engineering and organ-on-a-chip (ING-IND/34)

The teaching introduces the principles of tissue engineering, as well as an up-to-date overview of recent progress and outlook in this field, focusing on organ-on-a-chip technologies. To this aim, prior knowledge of cell biology and chemistry will be provided to build foundations to elaborate the important aspects of tissue engineering. 
The objectives are: 
•    to gain a basic understanding of the major areas of interest in tissue engineering 
•    to learn basic engineering principles of organ-on-a-chip technologies 
•    to understand the promises and limitations of tissue engineering 
•    to understand the advances and challenges of stem cell applications in the field

Computational models of physiological systems (ING-INF/06)

•    Provide theoretical contents for modeling excitable cell membranes 
•    Provide computational models aim at describing neuronal structures at different scale, from single neuron up to large-scale complex networks. 
•    Solve theoretical problems of neuronal computation


Track2: Robotics medicine

Biomedical robotics (ING-INF/06 + ING-INF/05)

•    Be familiar with the state of the art in applied medical robotics and medical robotics research 
•    Identify and describe different types of medical robots and their potential applications 
•    Understand the various roles that robotics can play in healthcare 
•    Understanding how the current state of the art in robotics-related technology, e.g., the internet of things, artificial intelligence, virtual/augmented/mixed reality can be applied to medical robotics.

Rehabilitation and assistive technologies (ING-INF/06)

After completing this course, the student will be able to: 
•    Develop the analytical and experimental skills necessary to design and implement methods and technologies (i) to help people with disabilities regain lost cognitive, sensory and/or motor functions; (ii) to evaluate the loos of cognitive, sensory and/or motor functions and their evolution with time or their change due to treatment. 
•    Be familiar with the state of the art of rehabilitation engineering both in the clinical practice and in research 
•    Know basic concepts of kinematics, dynamics, and control relevant to rehabilitation engineering 
•    Know the state of the art and the basic concepts related to prostheses (for missing limb replacement)

Surgical Robotics (MED/18)


Track3: Health Informatics

Cybersecurity and data protection

Parte giuridica (IUS/08)


Parte informatica (ING-INF/05)

•    Understanding the basic properties of cybersecurity: Confidentiality, Integrity, Availability 
•    Learn the methodologies to ensure such properties: cryptography bases, cryptography protocols, access control. 
•    Understanding the bases of data privacy and data anonymization 
•    Learn the main techniques for the anonymization of data that guarantee significance and interpretability of anonymous data

Healthcare information systems (<SSD>)

The course aims to give basic knowledge about healthcare database management systems, data modeling in healthcare information systems and an overview about graphical user interfaces and acceptance criteria. 
Part of the course will also treat healthcare information and integration standards and a clinical process description through “de facto” standards, with a final brief focus on data collection and reporting systems. 
•    To correctly use standard clinical terminological systems 
•    To understand and correctly use health information infrastructures 
•    To understand the implications of the General Data Protection Regulation (GDPR) to the clinical practice and research

Telemedicine (ING-INF/03)

•    Be familiar with the IoT and Context-awareness paradigm 
•    Be able to describe and explain Context-Awareness for remote monitoring of patients, home based and mobile self-management tools, hospital-at-home and remote diagnostics, telerehabilitation 
•    Learn smart-assistants (domotic systems, wearable systems, app) for interaction with patients


Optional courses

Biosensors and Microsystems (ING-IND/34)

At the end of the course the student will be able to: 
•    Interpret the working principles of the main biosensing mechanisms 
•    interpret and evaluate the technical specifications of a biosensor and evaluate its performance within a given application scenario 
•    describe the scaling effects on the physical and chemical properties of a system/device 
•    describe the main microfabrication technologies and evaluate their capabilities in microsystem development 
•    describe the applications and markets of microsystems for biomedical application

Advanced simulation in medicine (Marco Frascio MED/18) 
The aim of the course is to give an overview of advanced simulation in medicine and how innovative technologies can enhance medical training. The course will start with an introduction about current medical simulation methodologies. Then, the main technologies suitable for simulation will be described. This includes 3D printing, Virtual and Augmented Reality, Sensors for skills monitoring, Haptics, Robotics and Artificial Intelligence. Withing this framework, a critical analysis of existing tools specifically designed for medical simulation and hands-on activities will be included. Finally, the course will focus on research in simulation, with the final goal to teach attendees how to perform high-quality research.

Healthcare management (ING-IND/35)


Radiomics in medical images (MED/36)

Radiomics is a quantitative approach to medical imaging, which aims at enhancing the information from existing data available to clinicians by means of mathematical analysis. Through extraction of the spatial distribution of signal intensities and voxel grid relationships, radiomics quantifies textural information by using analysis methods from the field of artificial intelligence. Evidence in progress exists highlighting the potential of radiomics to enhance clinical decision-making. 
At the end of the course the student will: 
•    be familiar with the main procedures of image segmentation, image processing, feature extraction and qualification of CT, PET-CT, and MR images 
•    be aware of the current challenges of radiomics (e.g., reproducibility, big data management, data sharing, standardization) 
•    be informed on the potential of radiomics in clinical decision making to enable diagnosis, tumor prognosis and treatment decisions 
•    know the current status of the clinical literature in which radiomics analysis has been applied


Ultimo aggiornamento 2 Novembre 2023