Innovation corner: Using digital body replicas to combat respiratory infections
For patients fighting dangerous lung infections, digital twins can help battle disease with data
Reinhard Laubenbacher, PhD, and his team are building a digital replica of the body’s immune response for
respiratory infections to help future patients.
The subjects are: Reinhard Laubenbacher, PhD, director of the Laboratory for Systems Medicine at UF Health; Timothy "T.J." Sego, PhD, assistant professor of medicine (also in the Laboratory for Systems Medicine); Daniel Cruz, PhD, postdoctoral associate; and Luis Sordo Vieira, PhD, assistant professor of medicine.
Photo by Betsy Brzezinski
For critically ill patients fighting dangerous lung infections, a new ally is on the horizon: a digital twin that can help battle disease with data.
UF Health researchers are painstakingly building a digital replica of the body’s immune response to respiratory infections caused by microbes, bacteria, and viruses. Then, they’ll use a patient’s medical data to craft a personalized treatment plan.
“The starting point for a digital twin in medicine is really an individual patient,” said Reinhard Laubenbacher, PhD, a professor in the department of medicine and the director of the Laboratory for Systems Medicine at UF Health. “It’s about what we can do for a particular patient today, which is different from what we can do for them tomorrow.”
While the idea of digital twins isn’t new, their deployment in medicine and the life sciences is exploding. One estimate puts the revenue growth of digital twin development in health care at $21.1 billion by 2028 — a thirteenfold increase from 2023. Other digital twins are also
in development at UF Health, including a virtual replica of an intensive care unit room.
A digital twin offers distinct advantages for doctors. They are often bombarded with thousands of pieces of patient data. Time pressures are intense and patient responses can vary.
As a patient’s condition evolves, so does its digital twin. The twin lets doctors model treatments and see their potential effects before they’re used in patients. Harnessing the trove of patient data can make doctors’ decision-making faster and more accurate.
“The model’s predictive ability is continuously applied to the patient’s health state,” Laubenbacher said.
Building an immune twin that can make reliable predictions for doctors could take another five years or more. Laubenbacher estimates that a functional twin of the human immune system is a $1 billion effort.
“Knowing the entire pipeline — the patient, the model, and the biology — is key to the success of the project,” Laubenbacher said. “What you want is this digital twin to ultimately beaccessible from a tablet that an ICU doctor carries around.”
