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Their projects may focus on very different body parts, like the spine and the heart, but share the same goal: to understand how bodies work and break down, and what the best treatment is, using data-driven digital twins.
The digital twins of the future will be infused with data from clinical exams and your personal medical history. Aided by artificial intelligence, tomorrow’s doctors will detect diseases faster and treat them better. Digital twins can also help uncover the cause of diseases.

Medical detective work

For professor of Orthopaedic Biomechanics, Keita Ito, the fascination begins with the mystery of why some healthy children, mostly girls, develop a curved spine during adolescence. ‘The disease, adolescent scoliosis, has existed for thousands of years, and we still don’t know what causes it,’ he says.

Because it affects children during growth, collecting data, such as imaging or tissues, is ethically complex. So, Ito’s team builds digital twins of patients’ spines based on MRI and ultrasound data to study how they evolve.

These biomechanical models can simulate posture and visualize subtle changes long before they become visible in real life. They are not fantasy replicas but scientific instruments that allow doctors to study how a disease develops inside a living, growing human.

Predicting heart rhythm disorders

PhD studentʉ۪s work looks equally inward but focuses on a different vital organ. She studies ventricular tachycardia (VT), a dangerous heart rhythm disorder that can occur years after a heart attack.

Just like Ito’s spinal twins, Buck’s cardiac twins are built from real patient data such as electrocardiograms, lab values, and clinical reports.

‘We are trying to identify who is truly at risk,’ she explains. ‘Right now, doctors use measures such as left ventricular ejection fraction to guess who might develop VT. It’s imperfect, but it’s all we have now. With a digital twin, we can simulate the heart’s electrical and mechanical behavior and may be able to see how different conditions trigger arrhythmias before they happen.’

Prototypes and challenges

Together, these prototypes represent the broad power of digital twins in medicine. They link individual medical histories with results from clinical studies, physics-based models and machine learning to create predictive, dynamic systems that evolve along with their human counterparts.

In Ito’s lab, a spinal twin bends and straightens with virtual muscles and ligaments, testing hypotheses about the mechanics of posture and growth. In the project , ºÚÁϸ£ÀûÍø researchers, Philips, and Catharina Hospital are working together to develop a cardiac digital twin that maps the electrical pathways linked to ventricular tachycardia. The team is also analyzing clinical data from the hospital to explore whether it can improve predictions. Yet digital twins force researchers to rethink how data, ethics, and medicine interact. ‘Hospitals weren’t designed for research across populations,’ Buck says. ‘They are designed for patient care and data is only stored for that purpose, not to train predictive models. So, we spend a lot of time on collecting and cleaning data.’

Ito adds that models should never replace doctors but complement them. 

A digital twin helps generate new knowledge and predict outcomes, but doctors remain at the center of decision-making.

Prof. Keita Ito

From one-size-fits-all to individualized care

What makes this research so scientifically exciting is that it unites engineering, biology, and data science to tackle real clinical mysteries. Digital twins don’t just copy the body, they reveal how the body behaves under stress, how disease emerges from tiny mechanical or electrical failures, and how treatments can be tested safely before reaching patients.

Buck sees this shift as inevitable. ‘We’re moving towards data-driven healthcare, where one-size-fits-all treatments give way to individualized solutions,’ she says. ‘It means better outcomes for patients and smarter, more affordable care.’

In the end, what connects a growing spine and a healing heart is the same promise. Whether predicting scoliosis before it curves a child’s back or preventing a fatal heart rhythm before it strikes, digital twins offer a new lens into the living body, a vision of medicine that learns, adapts, and heals with us.