Module: Key Enabling Technologies for Biomedical Engineering

Transcription

1 Module: Key Enabling Technologies for Biomedical Engineering Mandatory Courses New Frontiers in Medical Technology Coordinator: Laura González Credits: 6 This course offers an up-to-date report on current and emerging medical technologies. Lectures are given by a core of experts who will provide extensive coverage of new medical technologies focusing on the state-of-the-art, current limitations, future perspectives and new applications. This subject provides knowledge of the needs, applications, and biological effects of medical devices and thus point the way toward new opportunities for engineering solutions. Each seminar is done by recognized specialists who describe current and emerging medical technologies including: advances in biomedical signal processing, medical imaging technologies, nanomedicine, tissue engineering, regenerative medicine, biomaterials, equipment monitoring, diagnosis and therapy, lab-on-chip devices, Medical devices: Design and Market Regulations Coordinator: Jordi Colomer Credits: 6 This course is designed for students with few or no prior knowledge in the design and conception of Biomedical Equipment and Devices. The course will introduce students in the most outstanding aspects (management, documentation, legal framework, ethics ) for the proper development of a medical or biomedical device. 1

2 Elective Courses: Data Science for Health Advanced Biomedical Signal Processing Coordinator: Santiago Marco Signal Processing is fundamental for the analysis of bioelectric and biomechanical signals, but also in state of art diagnostic instrumentation. Information about our health can be captured through physiological instruments that measure heart rate, blood pressure, blood and breath gases, blood glucose, brain activity and many others. Biomedical signal processing involves the analysis of these measurements to provide useful information upon which clinicians can make decisions. This course will be organized around selected cases where signal processing plays a key role. The students will learn the key underlying theoretical concepts. During labs students will challenged analyze the signals using high level programming languages. While supervision will be given, it is expected that the students follow a self-discovery path to each to the presented signal processing cases. Data Processing for Analytical Instrumentation Coordinator: Antoni Pardo The aim of the course is to present multivariate signal processing techniques for analytical instrumentation. These include exploratory data analysis, pre-processing, pattern recognition, event detection, etc. These techniques will be applied over datasets from hyphenated instruments like GC-MS or LC-MS, but also other spectroscopic devices as IMS or FTIR, can be source of data. The course has 10 sessions. Each session deals with a specific signal processing case. Sessions begin by stating a problem and a number of illustrations of it. Next, signal processing solutions are discussed and, finally practical MATLAB code implementations are done and some conclusions are extracted. Advanced Biomedical Imaging Coordinator: Domènec Ros Department: Department of Biomedicine School of Medicine and Health Sciences 2

3 We will study advanced methods in the field of medical imaging and how can we bring them to a medical/clinical sites. The student will have the ability to investigate among these methods and to search for a solution adapted to the customer's need. Topics to be covered include simulation and image processing in emission tomography, image processing in experimental magnetic resonance, analysis and diagnosis of intestinal motility through endoscopic images and analysis and characterization of atherosclerotic lesions from intravascular ultrasound images. Neuroimaging Coordinator: Roser Sala Department: Department of Biomedicine School of Medicine and Health Sciences Neuroimaging techniques represent a key tool to study the human brain and to understand how does it function. Neuroimaging is an emerging resource used in clinical and pharmacological studies. In this elective course, the student will learn the basics and the advanced concepts related to neuroimaging, in order to be able to search within the best methods to solve a clinical-related question. The objective is to identify the needs related to acquisition and processing of brain images and to find solutions with transferability to medical and clinical environment. ICT and Data Science for Personalized Medicine Coordinator: Josep Roca Department: Department of Medicine School of Medicine and Health Sciences Information and Communication Technologies (ICT) and Data Science for Personalized Medicine propose an innovative approach to our understanding of disease mechanisms, which should contribute to build-up systems medicine in clinical practice. The subject focuses on the role of ICT and data analytics to explore the emergent properties of biomedical data (big data) aiming at efficiently facing current healthcare challenges. Specifically, the subject will address deployment of integrated care and personalized medicine, as well as the use of predictive analytics for service selection. Ultimately, we will share recent experiences and provide skills to students while fostering future contributions into the field. 3

4 Elective Courses: Innovation in Tissue Engineering and Regenerative Medicine Advanced Technologies for Tissue Engineering Coordinator: Oscar Castaño This course offers an up-to-date report on current and emerging medical technologies. Lectures are given by a core of experts who will provide extensive coverage of new medical technologies focusing on the state-of-the-art, current limitations, future perspectives and new applications. This subject provides knowledge of the needs, applications, and biological effects of medical devices and thus point the way toward new opportunities for engineering solutions. Each seminar is done by recognized specialists who describe current and emerging medical technologies including: advances in biomedical signal processing, medical imaging technologies, nanomedicine, tissue engineering, regenerative medicine, biomaterials, equipment monitoring, diagnosis and therapy, lab-on-chip devices, etc. Advanced Biomaterials for Tissue Engineering Coordinator: Josep Samitier The aim of this subject is to provide the state-of-the-art knowledge of biomaterials for their application in tissue engineering and regenerative medicine. While providing a comprehensive overview of the field, the emphasis is on the design principles, fabrication technologies, physical characterization and biological evaluation of the biomaterials for tissue engineering. As outcomes of the learning process, the students should achieve the following goals: Knowing the different types of bio-materials and being able to select them for different tissue engineering applications, analyzing the behaviour and performance of biomaterials used in tissue engineering, knowing the principles of biocompatibility of biomaterials, knowing and applying the techniques for evaluating biomaterials and providing sufficient knowledge of biological principles involved in the interactions of the material with the receiving organism. 4

5 Organ-on-a-chip Coordinator: Romén Rodriguez This course presents the evolution of Lab-on-a-Chip systems for tissue culture to give rise to systems that are able to mimic the functioning of specific organs in humans, the so-called Organ-on-a-Chip systems. The course also presents different applications of Organ-on-a-Chip systems emphasizing the opportunities for drug screening and development, disease modelling and personalized medicine. The course will provide a practical approach to the experimental techniques used to build Organ-on-a-Chip systems. At the end of the course, the student will be able to propose new systems designed as accurate models of the structure and function of human organs, such as the lung, liver and heart. Organ Biofabrication and Regenerative Medicine Coordinator: Ramon Farré Department: Department of Medicine School of Medicine and Health Major diseases result in irreversible structural and functional organ damage, with organ transplantation as the only therapeutic indication when the disease reaches an advanced progression. However, the success of transplantation is limited, mainly due to the scarcity of organ donors and the incidence of alloimmune responses caused by disparities between the donor s and recipient s human antigens. The shortage of donor organs therefore requires a strategy towards increasing the availability of suitable organs for transplantation. This problem is enhanced by the progressive ageing of the population, which has lengthened the waiting lists of patients with severe diseases. In this context, organ bioengineering is now viewed as an innovative promising therapeutic alternative boosted thanks to recent advances in the field of tissue engineering and regenerative medicine. As detailed in the Methodology and Content sections, this 3-ECTS elective course is based on active learning from an interdisciplinary approach. The course is aimed at updating the current knowledge in organ biofabrication from a biomedical engineering perspective and, particularly, on identifying what are the open issues and the innovation roadmap required to accelerate translation of knowledge to the market and health care system. 5

6 Elective Courses: Design and Advanced Medical Technologies Wireless Biodevices and Systems Coordinator: Neus Vidal This module introduces students to wireless implantable and wearable devices and systems, with an emphasis on innovation and market opportunities. The module includes; market analysis and trends, basic background, technological keys, test and related Lab equipment, modelling techniques, as well as devices analysis and applications. Lean startup methodology as well as coaching tools and techniques are introduced by experts and applied during the semester. This course is aimed at teaching some key information and skills that are needed by professionals working in this or related fields. The module structure is based on project based learning. Technologies for Point of Care Coordinator: Pere Miribel The objective of this subject is to address the student in the conception and development of Point-of-Care Devices that combines different technological resources. Generally, the requirements of POC platforms are portability, low cost, easiness of use and the usage of small volumes of samples. It will attempt to point out the great complexity to combine interdisciplinary knowledge from Analytical Chemistry (Biosensors), Electronics and other Key Enabling Technologies and disciplines like nanoparticles to microfluidics. Nanomedicine Coordinator: Gabriel Gomila The course will present an overview of current and future emerging medical innovations based on Nanotechnology. In the first part of the course, the key enabling nanotechnologies for medical applications will be presented, including, micro and 6

7 nanofabrication technologies, nanomaterials technologies and nanoscale characterization and manipulation techniques. In the second part, some nanomedicine applications examples in current clinical use will be described, together with a description of the innovation and exploitation route followed. Finally, in the third part, an overview of future innovation and entrepreneurship opportunities in the nanomedicine field will be presented, together with some specific regulation aspects related to the exploitation of nanomedicine developments. Machine Learning Coordinator: Agustí Gutierrez Brief summary This is a graduate level course designed for students with no prior knowledge on machine learning. It explores major concepts of machine learning and their application in healthcare and biomedical engineering applications. The concepts of machine learning will include clustering, classification, and regression methods. And they will be used to detect arrhythmias, breast cancers; classify heart diseases in an automatic fashion among other pathologies. The students at the end of the course will be able to identify what biomedical problems can be addressed with machine learning techniques, choose and apply the more adequate technique and interpret and evaluate the results. 7