Digital Twin in Healthcare
The Digital Twin in Healthcare is a USE (User, Society and Entrepreneurship) Learning Line consiting of 3 modular courses in which Engineering students from multiple faculties explore some of the most pressing challenges of the medical sector, from concept design to implementation.
Burcu Gumuscu Sefunc, Regina Bernhaupt, Patrik Hummel, Wybo Houkes
Challenge owners (a TU/e teacher), clinical liaisons (medical practitionairs), industrial liaisons (software or sensors suppliers), TAs (coaching and connecting teams to managers of the challenges).
Industrial Engineering and Innovation Sciences
Generally, open-source software is used. Specific sensors can be requested by students teams in agreement with their clinical liaisons.
INTENDED LEARNING OUTCOMES (ILOs)
As specified in the study guide, the following are the expected learning outcomes:
- Design a digital twin solution for a medical problem by applying medical and technological knowledge.
- Integrate patient data into computational models to generate a digital twin as a minimal viable product for a specific medical problem.
- Integrate stakeholder requirements into a digital twin model and validate it in a medical setting.
- Achieve personal and team development growth to become a better engineer.
- Acquire effective communication skills in order to engage DTiH stakeholders.
The setup of this course consists of three quartiles (approximately two thirds of a full academic year) that students have to complete in sequence and that are all mandatory.
- Q1 (Conceiving Phase) is about understanding the medical problem: What is the medical problem? What do the clinicians use to solve that medical problem? This means learning about the anatomy, the physiology, and the cell biology. Moreover, depending on the challenge, the students need to solve equations and work with data to get a deeper understanding of the medical problem. The dominant discipline is Biomedical Engineering.
- Q2 (Designing Phase) is then focused on making data accessible and usable, through user experience design. The outcome is a minimum viable product. The dominant discipline is Industrial Design.
- In Q3 (Implementing and Operation phase) the students focus on bringing that idea into the market, taking into account all societal, ethical and economical considerations. The dominant discipline is Industrial Engineering and Innovation Sciences.
There are 6 challenges that the students can choose from:
- Anterior cruciate ligament reconstruction
- Diabetes and Metabolism
- Atrial and ventricular fibrillation
- Interactions between medical devices and human tissues
- Adolescent idiopathic scoliosis
- Hernia prevention, implants and fibrosis
The students work in teams, guided by a TA and in close contact with their clinical and/or industry liaisons, following an agile approach.
The course consists of a diverse portoflio of learning activities, distributed across the three quartiles (Q1, Q2, Q3).
- A camping trip at the start of Q1 in which students bond and choose their challenge;
- visits to actual surgeries (in Q1);
- one midterm test;
- a few short lectures;
- workshops on biosensors, on machine learning and artificial intelligence, and on user design and user experience (in Q2), next to teamwork, personal development and pitching workshops.
- feedback sessions with coaches;
- demodays, in which the outcome of the team work is demostrated to all stakeholders;
- an Art and Science Fair (at the end of Q3), to translate their concept into an artistic piece, with a budget of 50 euros per team;
Students are assessed on their presentations and their written proposals in each quartile, using specific rubrics. Teachers and TAs are the assessors.
The course is evaluated in a “postmortem”, a feedback sessions after each quartile with all the teachers from Q1, Q2, and Q3. Every teacher is in all three postmortems. Moreover, the teachers are expected to be present during the demo day, so they know what the progress and quality of the teams are. Students are also called to give feedback to improve each module during “sounding board” sessions.
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