A digital twin is a virtual replica of a physical, biological or virtual object. Used widely in manufacturing and other industries, digital twins are also becoming increasingly commonplace in healthcare. Digital twins are used for everything from testing drugs, exploring chemical compounds and predicting outcomes to assisting in surgical planning. As part of this new trend, doctors have started using 3D-printed models of organs and body parts to help plan surgery before an operation takes place.
Digital twins are a virtual representation of an object or process. They are used to represent objects and processes that are too big, too small or too complicated to physically model. With the rise of machine learning and artificial intelligence, digital twins have become particularly useful in the pharmaceutical sector as they can be used to simulate potential treatments and possible interactions with drugs and disease.
Digital Twin and Clinical Trials
Clinical trials are among the most significant events in medicine, but they also carry with them many challenges. The stakes are high, because every patient who is treated, and every doctor who sees that patient, can be one of the lucky ones.
The number of patients who enter clinical trials is a small fraction of the population, so there is a huge potential for bias. That’s why we need to be very careful when we talk about “the data”. The data used in clinical trials comes from the same sources as other clinical trial data: studies that have been conducted around the world and that have been approved by regulatory authorities. And while it’s true that clinical trial results can vary greatly across a certain type of study, it also happens to be true that a lot of this variation comes down to differences in design between studies…
There are two kinds of clinical trials: randomized controlled trials (RCTs) and non-randomized controlled trials (NCTs). When we say “randomized controlled trial”, we mean one where you randomly assign participants to one of two or more different treatment conditions. So there are two kinds of non-randomized controlled trial: observational studies where researchers simply ask people if they want to take part in an experiment, and cross-sectional surveys where researchers don’t assign participants at all and simply interview them about their health or behavior.
RCTs (sometimes known as randomized control trials) are very expensive to conduct but they provide us with the most accurate information because participants don’t know what condition they are on until after they have been assigned one or other treatment condition. They also tend to provide more reliable results than non-randomized controlled trials because you can use statistical controls to check for potential bias (for example, if you randomly assign participants into three different groups but then find out all three groups were actually similar).
While RCTs may seem like an ideal way to gather data on conditions like cancer or heart disease, they often contain some biases that make them less useful than observational studies (because you might be able to identify those biases by looking at how well each group performed relative to others). So what is happening here?
In observational studies, researchers sometimes look at how well people do relative to other people in their geographical location. The problem here is that if you want your study about heart disease or cancer patients compared with heart disease or cancer patients across the whole country then you need
How can data from sources such as clinical sensors or DNA help find personalized treatment options?
In the last few months, researchers at the University of Virginia and the University of Texas have been working on digital twin technology. They contend that it could revolutionize medical research and identify the right treatment for each patient, but progress is being held up by a lack of quality data.
The story is simple: in order to move forward with their research, they need to identify the right treatment for each patient. They have a technology called “virtual trial” which basically allows them to generate randomized controlled trials (RCTs) — where they are allowed to randomly assign people to receive different treatments or no treatment — in real-time. The advantage is that they can use this as a way to collect data and make decisions on which treatments are best for each individual patient based on all of his/her information.
They have developed an initial version of this technology, which allows them to do 1,000 virtual trials per day, meaning the data collected from one-thousand potential treatments will be sufficient for 10 days (or whatever length you choose). It takes about 5 minutes of training time per day for it to be set up and configured properly (it’s a bit more complicated than that), so we’ll need to do some testing here before putting it into production, but we can add some patients into our simulation model and see how it works.
But there’s still some work left, we need more patients or a longer “trial period” and we also need a little more time in between trials (which means adding an extra week), but hopefully we will get there soon enough!
What are the benefits of using digital twin technology in medical research?
The term “digital twin” refers to a computer-generated image of someone whose physical traits are similar to yours, but who is not actually you. In medicine, digital twins can be used to “boost” the accuracy of results from clinical tests or medical procedures.
In the case of cancer treatment, digital twins can help doctors find a more effective treatment for a patient with an aggressive cancer.
Digital twins can also allow physicians to treat patients with different types of cancer at the same time and in the same location. These special cases—in which a patient’s type of cancer is treated using different tools—are called combinatorial cancer treatment.
Researchers have begun using digital twins in clinical trials in the past decade, but there was no way to verify their accuracy until recently. Now, researchers have been able to use an imaging technique known as magnetic resonance imaging (MRI) to assess whether patients using a new treatment are identical or not. This method allows researchers to determine if they are measuring real differences in patients’ body parts or differences between individual MRI scans and those of other people who may have done the same test.
Currently, only two studies have used this technique on brain scans from healthy people: one study found that MRI scans from healthy people were better than CT scans for detecting brain abnormalities; and another found that MRI scans from healthy people were about 70 percent accurate for detecting brain abnormalities in patients with schizophrenia and related mental illnesses.
These results indicate that digital twins can be used as routinely as MRI scanners for medical research purposes; however, it is debatable whether this method will be adopted in clinical settings.
The first concern is cost: it costs money to analyze images from multiple patients at once (although other methods such as computer-aided diagnoses could address this). The second concern is whether it would be useful enough in practice: there may just not be enough cases where clinicians could use it without getting a lot more expensive because it will require more testing on each subject, which would take a long time. Yet another concern relates to privacy: everyone has their own body and we don’t want every single scan being dissected by everyone else who happens by our office…
What challenges need to be overcome before digital twin technology can be widely used in medical research?
From the very beginning, it was clear that digital health and medicine were going to be huge. The technology exists to do everything from tracking one’s health to curing cancer. It’s just a matter of getting it right (or at least more right than we are now).
The technical hurdles that need to be overcome include:
- No one knows exactly what diseases people actually suffer from or how they are treated
- There are no standard ways of measuring disease or treatment outcomes
- Most medical institutions have little, if any, data or can’t afford the data they do have
For many years now, we have been moving away from patients describing what needs to be done for their health and toward a higher-level problem solving approach for treating their disease.
Digital Twin technology will help move us back toward the patient-centric approach, not just because it is more effective in the long run but because we can start before there is any real proof of concept for its effectiveness in clinical practice, which is why it is so important to get started as soon as possible so that we can take advantage of any early-stage benefits that may develop in subsequent months and even years.
How will digital twin technology revolutionize medical research and identify the right treatment for each individual
The promise of digital twin technology is clear: it could revolutionize medical research and identify the right treatment for each individual. But we still have a long way to go before this revolutionary technology can be used to identify the right treatment or measure the efficacy of a drug.
In the future, digital twin technology will be used to identify the right treatment for each individual. By learning and modeling the body’s responses, medical professionals can provide a more precise course of treatment. This could save lives and prevent patients from being prescribed treatments that will either do nothing or cause unintended harm to their bodies.
Digital twin technology will revolutionize the way medical research is conducted, providing patients with more accurate treatments. In practice, digital twins allow researchers to simulate physical systems and explore their behaviors in greater detail. Improvements in simulation accuracy over time will lead to more informed decisions on how to treat each individual.
I was inspired to write this article after my conversation with Prof. Dr. Koen Kas, CEO at HealthSkouts. In this episode, we talked about the Future of Healthcare, Wearables , Digital Twins Tech and much more. Furthermore, Prof. Dr. Koen Kas gives incredible insights about the future of Healthcare, specific innovative examples from the Wearables world. As well as how the healthcare business model of the future can be built in the Hospital.
Watch Episode #31 of Digital Health & Wearables Series:
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