Artificial Intelligence (AI) is revolutionizing how doctors predict, find, treat and prevent disease. Physicians, researchers, data scientists and others at the Brigham are engaged in a dazzling array of AI projects across many different disciplines. The story below is excerpted from the winter 2019 issue of Brigham Health Magazine.

Interested in reading more about AI? Catch CRN’s coverage of the First Look talks at the World Medical Innovation Forum, the theme of which is the intersection of AI and health care.

Among the wide range of artificial-intelligence-driven precision medicine projects underway at the Brigham is a robust effort to determine how to prevent patients from getting sick from the Clostridioides difficile bacterium. The most common hospital-acquired pathogen in the U.S., C. difficile can cause symptoms ranging from diarrhea to lethal colon inflammation and recurs in 25 percent of patients.

Georg Gerber, MD, PhD, MPH, FASCP, chief of computational pathology in the Department of Pathology and co-director of the Massachusetts Host-Microbiome Center, leads cutting-edge research efforts to tackle C. difficile. He is one of the few investigators in the world who designs sophisticated AI models to understand how the microbiota in the gut — beneficial microbes that colonize our bodies — may enable or thwart C. difficile infections in patients. With these findings, he and his colleagues will develop new diagnostic tests and treatments.

“Our machine learning tools are determining which microbes and their activities in the gut can prevent C. difficile infection in the first place, or limit recurring infection,” Gerber said. “Using that information, we could then develop diagnostic tests to predict who is susceptible to the infection, and ultimately create personalized therapeutics from living beneficial bacteria to prevent the disease or its recurrence.”

The amount of data to process for this work is immense, especially since the microbiome is an always changing ecosystem of trillions of microbes.

“It’s not only the number of variables we’re measuring but also the complexity,” Gerber said. “We can’t look at the organisms independently; we have to look at how they interact.”

According to Lynn Bry, MD, PhD, director of the Massachusetts Host-Microbiome Center at Partners HealthCare, Gerber’s algorithms have dramatically cut down the time and effort that would have been required to conduct the research without an assist from AI.

“When you’re trying to think through all possibilities on the microbe side and the host side, you can get lost,” Bry said. “If I had to test all possible combinations of which specific microbes are needed to prevent C. difficile infection, that’d be over a million possibilities for just 20 microbes. It’s not something you’re going to eyeball; it can’t be done in a spreadsheet. Georg’s algorithm gave us a short list of combinations to test.”

With that targeted data in hand, Gerber, Bry, and Jessica Allegretti, MD, MPH, director of clinical trials in the Brigham’s Crohn’s and Colitis Center, were able to identify which microbes in the gut prevent and treat C. difficile in animal models of the infection.

“The set of bacteria we found can cure C. difficile in mice. It’s amazing — mice who are very sick fully recover,” Gerber said. “The next step will be to do clinical trials in people to see how well this works.”