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Your Digital Twin Can Save Your Life
Article, Innovation, Precision Medicine, Wellbeing

Your Digital Twin Can Save Your Life

A digital twin is a virtual or digital copy of a living creature or a component of that organism, such as the heart or lungs. 

Data points such as image records, in-person measurements, lab findings, and genetics are used to construct a digital twin. 

All of this information is then utilized to create an identical digital copy of a person.

Over 250,000 fatalities could be avoided each year. 

According to Johns Hopkins University, this is the number of individuals who die each year as a result of medical errors.

Your Digital Twin

A virtual depiction of a real thing throughout its lifespan is a straightforward notion to grasp. 

The technology and its applications in healthcare, on the other hand, has the potential to be limitless and revolutionary.

Dr. Marko Grobelnik, AI researcher and Digital Champion Slovenia, as well as chief technical officer, International Research Center on AI at the Joef Stefan Institute, made a compelling case for why Digital Twin Technology will be a critical step in the evolution of healthcare in a brief discussion. 

Healthcare institutions, he said, are complicated systems with many interconnected operations that can only provide outcomes if they are correctly coordinated. 

Each of the procedures, he added, has some level of ambiguity, which might be detrimental or produce an unintended domino effect with potentially devastating effects. 

Because it runs with the breadth of observation and speed of reaction that humans lack, digital twin technology can assist in navigating such a complex system through a sequence of known and unknown challenges.

We could speed up work at an increased speed if digital twins were fully developed, saving time and, most importantly, lives.

The Digital Twin In Practice

Once a digital twin has been produced, it will be used in healthcare as a test dummy for illness or injury therapies before being used on individuals or groups of people.

Then, with plenty of biomedical information, we may develop more reliable and realistic medicinal therapies that are customized to each patient. 

Due to fast advancements in computer strength and the capacity to crunch extraordinarily huge quantities of data, this is becoming ever more feasible.

With adequate information about the genetic code, a personalized genome sequencing for a person with malignant tumors might be produced. 

An AI system might monitor and study the activity and reaction of these cancers digitally, and then build a customized biological weapon to attack the tumors. Conventional therapy has three benefits that these agents do not:

  1. Because they’re tailored to the person and their particular cancer kind, the probability of succeeding is significantly increased.
  2. Because they’re made of the patient’s DNA, they won’t be rejected by the body.
  3. Radiopharmaceuticals do not have the same negative side effects as these agents.

Ultimately, these compounds must be able to be injected or even swallowed as a tablet.

Because digital twins are digital models, they may be examined to deliver the sufferer advice on how to avoid chronic sickness, preventative maintenance, and even efficiency upgrades. 

Not just a treatment, but a manual for maintaining one’s wellness and avoiding disease in the future.

The fact that it is not our present situation is due in major part to the human genome’s enormous complexity and the seemingly infinite ways in which it may react to diseases and medication. 

Notwithstanding this current stumbling barrier, there are ongoing initiatives with promise.

To begin, the UK government launched the 100,000-genome project, which aims to analyze the whole chromosomes of 85,000 individuals in the National Health Service. 

The study focuses on cancers and infectious illnesses that are prevalent. 

In the United States, projects like Human Longevity Inc. and the Mayo Clinic Centre for Individualized Medicine collect genetic data on large groups of people, giving researchers and engineers additional data to work with.

The Applications of a Digital Twin

Though the purely digital twin we’ve been talking about thus far is still a ways off, there are some present practical utility instances that show we’re getting close, the first of which is patient monitoring.

Smartwatches are a reality for many of us today.

We have fully adjusted to and regarded this equipment as ubiquitous, whether it is a Fitbit or other health and fitness monitors.

This same technology may be used to send real-time data to a cloud-based digital twin that will create models to detect disease signs early on.

The second step is operation simulation and risk evaluation.

Sim Cure, a firm headquartered in France, has created a patient-based digital twin for the treatment of lesions.

Sim Cure helps neurosurgeons enhance patient safety during therapy by using simulations and digital twins.

It’s not simply practiced on a simulated patient; it’s actual patient practice.

The third type of choice assistance is for diagnostic and therapeutic.

Healthcare providers will have a full and real-time image of a patient’s prior and present health state thanks to data from many medical resources such as radiology archives, in-person measures, laboratories, and genetics.

After that, the digital twin will replicate the patient’s health state, and AI technology will fill in any blanks with the most precise, pertinent, and evidence-based evidence gathered.

There are many startups entering the healthcare sector with the aim of building a digital twin.

For example Sanome, a startup revolutionising diagnostics through their at home diagnostics kit will bring enhance their offering by developing the digital twin. Read more about Sanome here!

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Photo by 20twentyphotos on Envato

Digital Twin Technology Destiny in Health Care Services

In the field of healthcare, the ability to construct digital twins exists.

The task before the healthcare sector is to begin evaluating and applying this technology to specific challenges.

By sequencing the RNA of mice with rheumatoid arthritis, scientists at Linköping University in Sweden produced digital twins of the animals.

The medicines that would be most successful at treating individual mice were then determined using computer simulations.

If properly executed, we will be able to wave farewell to human clinical trials permanently.

We can test all vaccinations and medicines on digital twins, saving lives quicker and eliminating the need to trial possibly hazardous therapies on people.

This is the kind of future that digital twin technology can bring about.

And this is the kind of future we require.

Conclusion

In the latest days, there has been a change in the usage of Digital Twins, aided by an increase in the number of papers published and industry leaders spending substantially in creating Digital Twin technology.

Without the similar rise in AI, IoT, and IIoT, which are becoming important enablers for Digital Twins, it would not be feasible.

As demonstrated by the huge number of publications on this topic examined above, the bulk of Digital Twin studies is concentrated on the industrial industry.

Regardless of the fact that the area of Digital Twins is still in its early stages and is controlled by manufacturing.

For example, we can see applications for the Digital Twin in power-generation equipment, big physical structures and the automotive industry.

The Digital Twin in healthcare is still in it’s early days but it is definitely a technology to watch!

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