Key Insights from the Digital.Health Webinar
Digital twin technology is rapidly reshaping the healthcare landscape, offering new ways to optimize medical treatments, device development, and patient care. In a recent Digital.Health webinar, industry experts, including Ami Bhatt (Chief Innovation Officer at American College of Cardiology), Simon Sonntag (Co-Founder & CEO of Virtonomy), Roozbeh Jafari (Principal Staff at MIT Lincoln Laboratory), and Cahal McVeigh (Research Director at Medtronic), explored how digital twins are already making an impact and what the future holds.
Here’s a recap of the most important discussion points and real-world applications of digital twin technology in healthcare.
Why Do We Need Digital Twins in Healthcare?
Ami Bhatt highlighted the growing necessity of digital twins, emphasizing that:
- The sheer volume of medical knowledge and data demands AI-driven solutions to harness the full potential of modern science and personalized care.
- In the future, conducting trial-and-error testing directly on humans may be considered unethical when digital twins provide a safer, more controlled alternative.
- Advances in computing power, data availability, and AI infrastructure will soon make digital twins more accessible across healthcare applications.
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[Video snippet: Ami Bhatt on why we need digital twins.]
How Are Digital Twins Already Being Used?
Digital twins are proving invaluable in medical device development, surgical planning, and clinical trials. Simon Sonntag and the panel shared several real-world examples of how they are used today:
1. Medical Device Development & Optimization
Before a medical device reaches human trials, digital twins allow developers to simulate performance of a medical device across hundreds of patients. It is possible to iterate on the design, identify issues, and optimize it at the stage where it is still cost-efficient. There is no need for real-world testing until you make sure the device design is optimal for the specific use-case.
2. Enhancing Animal Testing Trials
By utilizing a database of different animal types, breeds, and sizes, clinicians can decide on the suitable animal, and implantation strategies, and go into animal trials with more confidence. By doing so, they can cut the trial-and-error and find the correct animal to reduce the number of animals used.
For example, Scandinavian RealHeart (developing a TAH), had successful animal trials after choosing the correct breed and animal type virtually by implanting their device CAD model into digital twins of different animals using our software v-Patients. This lead to a successful round of animal trials.
Read more about their testimonial here: Customer Success Story: Using v-Patients to Ensure Positive Outcome of Animal Trials
3. Adapting Devices for New Populations
When transitioning to a new market or a new population group (such as e.g. Japanese patients) which might have different anatomies compared to other population groups, using digital twins can help in making sure the device fits well and knowing which adjustment need to be made, before even going into real-life trials.
4. Surgical Planning
As an example, for pediatric patients, size constraints present unique challenges as their chest sizes are small. This makes it hard to make decisions. Digital twins assist clinicians in:
- Simulating surgical procedures before going into the operating room.
- Improving decision-making and making operations safer for children.
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[Video snippet: Simon Sonntag on using digital twins for device design optimization]
What are Digital Twins? The Five Components of a Digital Twin in Healthcare
According to Roozbeh Jafari, a functional digital twin in healthcare consists of:
- The actual physical instance (e.g., a patient, organ, or medical device).
- A virtual representation that closely mimics the physical counterpart – The model has to closely reflect e.g. the actual cardiovascular system to be actionable.
- Real-time data flow from the physical to the virtual model via sensors – the virtual representation needs to be updated dynamically and has to remain tightly coupled.
- Feedback from the virtual twin to the real-world instance, aiding decision-making. Physicians can use the digital twin to figure out how the patient will react to an intervention and what effects it will have.
- Collaboration among stakeholders (physicians, engineers, and researchers) to refine the model.
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[Video snippet: Panelists discuss the meaning of digital twin]
How does the future look like for the usage of digital twins?
Cahal McVeigh from Medtronic shared a compelling real-world example of how digital twins are aiding in replacing traditional clinical trials with in silico (virtual) trials within his company.
In their neurovascular division, they created hundreds of virtual patients with brain aneurysms and were able to virtually implant their device into them. In this way, they were able to demonstrate to regulators that they have favorable performance compared to real-life clinical trials. Therefore, there was no need to replicate those trials.
According to him, this shift toward in silico medicine is a game-changer for regulatory approval and personalized healthcare.
What kind of partnerships and collaborations are going to be needed to accelerate the adoption of digital twin technologies?
Cahal McVeigh predicts that a lot medical device companies will soon be moving to digital tools, and putting more focus on digital twins. He says that at the moment, digital twins are mostly used for optimizing devices and optimizing their design to work in as many patients as possible. In the future these digital twins might be supercharged, and the device might be more optimized for each individual patient. This will lead to more real-time predictions to plan a procedure, and options to update the plan on the fly, leading to responsive digital twins. We might see this especially in clinical applications, with the recent trend towards robotics. He concludes that we also need close collaboration with regulators, and/or need to reinvent the regulatory process.
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[Video snippet: Cahal McVeight on the usage of digital twin technology for real-time predictions]
Simon Sonntag predicts the rise of precision medicine, where every patient could get personalized suggestions based on the data. The regulatory part is also important, even more so on a global level. We need a collaboration worldwide to make it safe and applicable. This can then lead to fully in silico trials.
As Roozbeh Jafari pointed out, physicians should not have to guess what intervention would be the best, but actually base it on hard facts coming from these simulations and from data gathered through the use of digital twin to have more certainty.
Final Thoughts
As Simon Sonntag pointed out, the ultimate goal of digital twins is to enable precision medicine—where every patient receives tailored treatments based on their own virtual model.
With advancements in AI, computing, and global collaboration, digital twins are set to revolutionize healthcare, making treatments safer, faster, and more efficient than ever before.
For the full recording of the webinar, follow this link: Digital.Health Studio Series – Digital Twins Meet Healthcare Transformation
