OUR education at a Glance


At TU/e we organize DTiH in three areas: disease description and observation, model creation and knowledge generation. We recognize the momentum put forth by countries and private healthcare organizations to expand the research, knowledge, and applications of the digital twin in healthcare. Hence we have created this USE learning line at TU/e. DTiH will not only create digital twins of medical devices but of patients, organs, organoids-on-chip, tissues, and acute and chronic diseases. The objective is to teach our students to take the first steps in creating a digital twin that can be employed a) to predict the physiological or pathological response of organs to pharmacological agents, b) to evaluate and improve radiological interpretation and diagnostic approaches, c) to monitor in real-time computational models of biological structures and systems (from biochemical pathways to cells, tissues, organs and individuals) or d) to optimize the effect of a particular surgical intervention prior to the actual surgery. Therefore, besides engineers of various disciplines and computer/data scientists, we collaborate with teaching assistants and coaches from medical and biology schools to form interdisciplinary teams and dissect a complex, multi-level research question in a wholesome manner.

Learn more about the Course Structure.

DTiH Mission

We aim to create a robust course for learning to solve pressing biomedical challenges using state-of-the-art digital twin concepts in healthcare. The course is targeted to undergraduate engineering students so they can learn both technical and humanistic skills useful in their professional careers.  

DTiH Vision

We are committed to generate quality eduaction through the creation of three phases:

1. Conceiving, 

2. Designing, and 

3. Implementing & Operation. 

Conceiving: In this first phase of the USE LL, the students deliver a minimal viable product (MVP) which represents the digital twin solution. Finally, the a minimal-viable-product (MVP) is pitched to an expert panel. 

Designing: The second phase focuses on the technology and realization competence necessary for prototyping Digital Twins. The course introduces a design space of digital twins and focus on three areas in designing DT: (1) User Experience (UX) and User Interface (UI), (2) data inquiry through sensors, (3) data visualization and processing using Machine-Learning basic models. Providing basic grips to each of these topics through workshops, the course supports the students through coaching sessions to apply the knowledge in the context of their medical cases.

Implementing and Operation: After conceiving a digital twin solution (Q1) and developing a concrete design (Q2), students shift their focus towards implementing the MVP product in practice. In the first half of the Implementation phase, students map the value spaces on which the digital twin solution operates. This exploration involves systematic attention to the attitudes and perspectives of various stakeholders, including alignments and tensions in value judgements across different stakeholder groups. In the second part of this phase, students develop a roadmap to navigate this value space and take first steps to get the MVP product ready to be a success in clinical practice. 

The technical and humanistic elements behind ‘Digital Twin in Healthcare’ (DTiH).