![]() ![]() HeartModel is not an empty digital shell when it is fed with images of your heart. HeartModel also calculates how well the heart is pumping out blood, which is an important indicator of possible heart failure. Patient, based on a set of 2D ultrasound images. It automatically generates 3D views of the left heart chambers of a In 2015 we launched Philips HeartModel – a clinical application that allows cardiologists to assess several cardiac functions that are relevant to diagnosis and treatment of patients with CVD. Unlike traditional anatomical models, which describe organs in a general way, a digital twin reflects the particular – it captures the idiosyncrasies that make your heart unique. How to get a good understanding of a patient’s heart? Think digital twin. This limits the usefulness of general, fixed anatomical models that are based on average population data. For example, your heart chambers may be shaped slightly differently than mine. Yet this is a crucial step in understanding the nature of a CVD and in planning and guiding interventions.Īdding to the complexity is that every heart is different. Medical images provide a wealth of information, but it is difficult for a clinician to reconstruct and interpret the anatomy of a patient’s heart from a set of 2D images. However, despite significant advances in medical imaging techniques like MRI, CT and ultrasound, determining optimal treatment plans for patients with CVD remains challenging. Cardiovascular diseases (CVDs) take the lives of 17.7 million people every year – almost one third of all deaths worldwide.Įarly detection and prediction of the progression of CVD are essential for saving lives through improved treatment. Your heart is as vital to your health as it is vulnerable to disease. But there is one part of the body where digital twin technology already has demonstrated promising applications, and that is the heart: the pump that fuels life.Įvery heart is unique – and why that poses a challenge to clinicians We are a long way off from having a full digital patient. As one surgeon said, “I see the digital patient as an opportunity to bring together all the information on a particular patient.” This could support diagnosis and treatment planning, and better targeted therapy delivery, or lifestyle interventions. The ultimate vision is to have a lifelong, personalized model of a patient that is updated with each measurement, scan or exam, and that includes behavioral and genetic data as well. The basic idea of such a “digital patient” is the same: if you integrate different measurements of a person over time, you can build a digital model of a body part such as an organ, and eventually an integrated model of their anatomy and physiology, so you can better understand how these function. If a digital twin of an MRI scanner can help you predict when a physical part needs replacement, and guide repair, could we apply the same concept to discover and treat ailments in the human body before they become apparent? So what about the following, fascinating, question: As outlined in my previous post, we have already taken the first exciting steps in this direction. Digital twins will enable us to analyze systems remotely in real time, prevent problems before they occur, and test new products in virtual environments before building them. Rather, it is bridging the physical world of people and objects with the virtual world of digital information.Ī prime example of this are ‘digital twins’: virtual models of systems that are updated dynamically by being connected to their physical counterparts, using a diverse set of sensors. Perhaps the most exciting part of the digitalization of healthcare is not digitalization per se. ![]()
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