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Friday, February 25, 2022

Medical Digital Twins

 I worked at the University of Florida on modeling medical procedures.   This would have been a very useful modeling extension. 

Medical Digital Twins: a New Frontier  By Allyn Jackson, Commissioned by CACM Staff, February 24, 2022

Last year, a new product for the treatment of type-1 diabetes came on the market: a "digital twin" of the human pancreas. The patient is outfitted with a bloodstream sensor and an insulin pump. The sensor continuously sends data about insulin levels to a device that looks a bit like a cellphone and that runs a mathematical model of glucose metabolism. The model is calibrated to the patient's health status and individual characteristics, such as gender, age, weight, and activity level. The model is linked to a closed-loop control algorithm to drive the pump, which when needed injects the required amount of insulin.

Not only does the digital twin free the patient from the need to pinprick for blood samples several times a day, it also optimizes the amount of insulin administered—just like a healthy human pancreas.

With the success of this kind of model, researchers are starting to envision development of a full-blown "medical digital twin," a software instantiation of the total health status of a person.  One leader in this effort is Reinhard Laubenbacher, director of the Laboratory for Systems Medicine at the University of Florida.

The challenges of medical digital twins are enormous, but Laubenbacher, who received his Ph.D. in mathematics from Northwestern University in 1985 and has spent the past 20 years in systems biology, is ready for it.  "As they say, go big or go home," he said.  "At this stage in my career, my life, that's what I need to do."

Digital twins are used extensively in industry. For example, a digital twin of a jet engine draws on real-time data from sensors in the physical engine to make short-term predictions about the engine's functioning. The twin can make adjustments to head off failure or optimize performance, and can identify faulty or failing components to be checked at the next maintenance. The most sophisticated digital twins are able to self-improve, by learning from situations in which their predictions diverge from what actually happens.

A medical digital twin would take health information about an individual, including data from sensors attached to the person's body, and feed that into a model comprising all major biological systems, from the organ to the cellular and even to the molecular level. Doctors could use the digital twin for a variety of purposes, such as predicting how that particular individual might respond to a given treatment.

However, such comprehensive, detailed models are far in the future. As Laubenbacher put it, "We are at step -1."   ... '

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