Joint research day - Tampere University & University of Twente

This area focuses on various research activities in cell and tissue engineering. It includes Organ-on-chip research where the aims are to investigate the safety and efficacy of health products and to understand the mechanisms of diseases, for example. Organ-on-Chip technologies are expected to find widespread use in biomedical science and progressively reduce the need for animal models as the mainstay in efficacy and toxicity testing. Topics include but are not limited to materials science, including hydrogels, micro- and biofabrication, microfluidics, sensing, imaging, in silico modelling, stem cell technology and tissue engineering. Called topics include applications of the technologies, for example, in the development of personalised treatments for patients in various disease areas.

This area focusses on the use of eHealth to empower people to manage their own health & well-being, and to bring care into the homes of people around the globe. It uses innovative research methods to develop and evaluate personalized interventions and technologies, combining data science, technology and psychology in an interdisciplinary and participatory approach to develop meaningful and sustainable health-promoting solutions. eHealth also allows addressing the challenge of diversity by offering personalized solutions that can be adapted to each user, and to changes over time. Every individual, dealing with any specific condition presents a distinctly unique case, making personalized eHealth support essential. As various data types are becoming increasingly available for medical doctors, researchers, and patients in the modern healthcare, methods and practices for efficient data analysis and interpretation are well warranted and hold great promise for the future of personalized medicine. Health data science comprises the analysis of diverse data sources to improve human health. These data include biomedical and clinical research data from patients, clinical data from hospitals or population registers (Real-World Data), and data from smart consumer devices.

This area’s focusses on the specific unmet needs and unresolved problems in various life phases of women. The goal is to increase knowledge of women’s physical and mental health, including the anatomy, physiology and pathology of the female body, as well as overall well-being throughout the lifespan. This research can help explain why differences between men and women occur in health and disease manifestation, and why women often respond differently to treatments. In doing so, we can develop or adapt technologies that can be used in the prevention, diagnosis and treatment of diseases that (primarily) affect women. These include women-specific diseases, such as gynecological disorders, breast cancer, and ovarian cancer as well as diseases that occur more often in women, such as osteoporosis. Here we focus on bringing innovative technologies specific for women’s health to those in need, thereby supporting personalization of prevention, diagnosis, treatment and aftercare, consequently leading to improved results in Women’s Health. The focus hereby is to improve women’s health and wellbeing across different stages of life, such as pregnancy, lactation and (post-)menopause – thereby benefiting women, their children and loved ones, and ultimately society as a whole.

This area focusses on enabling healthcare professionals to improve patient care by using accurate, quantitative, personalized multimodal and multiscale methods of imaging for screening, diagnosis and evaluation. It also improves their ability to practice precision medicine. The research ranges from development, demonstration and assessment to the clinical testing of new technologies and methods. Application areas include anatomic and functional imaging of vesicles, cells, tissues, vasculature and organs to diagnose and characterize disease and health. We work in the fields of applied physics, technology development, mathematics, translational research and clinical practice. Research groups [MS1] work across a wide range of imaging modalities – including ultrasound, optical, photoacoustic, molecular, magnetic, nuclear and micro-computed X-ray tomography – used in precision medicine.

Physiological sensing and systems are important for many domains and have important clinical and consumer health and wellbeing applications. Increasingly, clinicians diagnose and classify diseases using information collected from multiple physiological sensor signals. This area is skilled in measuring – and influencing – the body’s physiological signals to monitor bodily functions, assess the impact of (chronic) illness or trauma, and evaluate how treatment or a healthy lifestyle choices affect physiological functioning. It provides a deeper understanding of the physical principles of electricity, magnetism, mechanics, and fluids, as well as the anatomy and physiology of human functional systems of interest. These systems include the central nervous system, the cardiopulmonary system, the endocrine system, and the human movement system.

Realizing new solutions for cancer patients by connecting medical science with the innovative power of integrated biomedical and technical researchers, that’s what this track is about. This area consequently focuses on different innovations for future oncological care, such as imaging, data technologies, liquid biopsies, organoids and other advanced in vitro models, support tools for cancer care and much more. The thematic area covers topics from fundamental to highly applied cancer research including fields such as lifestyle data, early diagnostics, minimally invasive techniques, and optimized aftercare. It also covers more social domains in cancer care, such as measuring quality of care, and the development of psychosocial care and support, such as developing new methods to help cancer patients cope with fatigue following cancer treatment.

Optimised care planning focuses on improving the design, coordination, and implementation of clinical care pathways using data-driven methods, predictive modelling, and decision-support tools. Research in this area aims to ensure that patients receive the right care at the right time by integrating clinical evidence, patient-specific data, operational constraints, and health system dynamics into optimised treatment and care strategies. The theme covers topics such as risk prediction, resource allocation, clinical workflow optimisation, and personalised treatment planning across different medical domains, directly contributing to improved patient outcomes, reduced delays in care, and better support for healthcare professionals.

Digital twins are one example of how optimised care planning can be implemented. They can be used to create virtual models of patients, care processes, or healthcare systems, allowing researchers and clinicians to simulate different scenarios, predict outcomes, and evaluate the impact of interventions before applying them in real life. By combining insights from health data science, medical imaging, sensor data, and clinical expertise, these models support more efficient, personalised, and evidence-based care.