Technology : Digital Twinning

A digital twin is a virtual representation that serves as the real-time digital counterpart of a physical object or process.

The first practical definition of a digital twin originated from NASA in an attempt to improve physical model simulation of a spacecraft in 2010.

Digital twinning involves visual modeling software to replicate an object that looks similar to the real-life object, then feeding the system with data about that object.

Digital twin technology uses machine learning and AI.

You can train the digital twin to behave like you, but also you can take off all of the errors and things that you don't want in the digital twin, giving control over the behavior of that digital tool.

Inside the computer and virtual world, digital twins are upgradable and extendable, without the physical limitations experienced in real life.

With the help of a digital twin, companies can test and validate a product before it even exists in the real world.

If you provide a patient model with data and train that patient model with parameters that exactly look like the real world, it will give you similar results.

Going forward, there will be a transition towards cheaper, more accessible, and easier to use digital twins, culminating in its democratization.

Digital twin technology can be used to generate a virtual healthcare facility or a human body.

Digital twins can be created from an individual's genetic makeup, physiological characteristics, and lifestyle habits.

AI helps in the design of digital twins to put together organs and physiological data to output a 3D image, which can then be modeled to a specific patient from their specific parameters.

Digital twins of human hearts can visualize circulation patterns.

Digital twins can model potassium overload in the heart, which induces arrhythmias, to help understand, predict and avoid cardiac arrests.

Digital twins can model a heart restarting after surgery, showing imbalances of electrical signals going through the heart before an actual heartbeat.

A digital twin of a patient's heart combined with data from similar cases can predict the best possible outcome for the patient.

Digital twins enable doctors to simulate various options for a certain procedure and select the one with the optimal computer result.

The Swedish Digital Twin Consortium aims to develop a strategy for personalized medicine based on constructing unlimited copies of network models of all molecular, phenotypical and environmental factors, treating those digital twins with thousands of drugs to identify the best performing drug, then treating the actual patient with this drug.

Healthcare planners, practitioners, and administrators already use digital twin technology to aid robotic surgery, accelerate precision medicine applications, and manage inventories or complex supply chains.

Digital twinning with artificial intelligence is already supporting practicing doctors, improving healthcare access, and lowering costs.