A.I. ‘digital twin’ microbiome model may predict infant neurodevelopment

Using data from fecal samples collected from preterm infants in the neonatal intensive care unit (NICU), researchers from the University of Chicago report that their model, called Q-net, can predict which babies were at risk for cognitive deficits with 76% accuracy.

The gut microbiome is known to have a profound impact on the health and development of infants, but understanding how gut bacteria interact, and how these interactions may lead to gastrointestinal diseases and neurodevelopmental deficits, is difficult and time consuming through traditional laboratory experiments.

“You can only get so far by looking at snapshots of the microbiome and seeing the different levels of how many bacteria are there, because in a preterm infant, the microbiome is constantly changing and maturing,” said the study’s senior author Ishanu Chattopadhyay, PhD, assistant professor of medicine, in a press release. “So, we developed a new approach using generative AI to build a digital twin of the system that models the interactions of the bacteria as they change.”

The ‘digital twin’ concept is a potentially transformative technology. Writing in Science Advances​, the authors explained that a ‘digital twin’ is a digital representation of a complex system, “enabling the simulation of perturbations; study of trajectories, aberrations and failures; and the execution of high-fidelity simulation experiments that would otherwise be unattainable in the real world.

“Rather than answering a single question, a digital twin aims to mirror the entire system, distinguishing it from typically more limited standard machine learning models,” they explained.