Objective: Chagas disease is a parasitic infection that is endemic to South America, Central America, and, more recently, the U.S., primarily transmitted by insects. Chronic Chagas disease can cause cardiovascular diseases and digestive problems. Serological testing capacities for Chagas disease are limited, but Chagas cardiomyopathy often manifests in ECGs, providing an opportunity to prioritize patients for testing and treatment. Approach: The George B. Moody PhysioNet Challenge 2025 invites teams to develop algorithmic approaches for identifying Chagas disease from electrocardiograms (ECGs). Main results: This Challenge provides multiple innovations. First, we leveraged several datasets with labels from patient reports and serological testing, provided a large dataset with weak labels and smaller datasets with strong labels. Second, we augmented the data to support model robustness and generalizability to unseen data sources. Third, we applied an evaluation metric that captured the local serological testing capacity for Chagas disease to frame the machine learning problem as a triage task. Significance: Over 630 participants from 111 teams submitted over 1300 entries during the Challenge, representing diverse approaches from academia and industry worldwide.

Fox News senior medical analyst Dr. Marc Siegel shares his perspective on whether the mosquito-borne virus in China will spread to the United States and how AI can be detrimental to children's and young adults' mental health on'Fox Report.' A little-known disease is spreading in the U.S., primarily in the state of California, health officials warn. In a new study published in the CDC journal Emerging Infectious Diseases, researchers state that human cases of Chagas disease have been confirmed in eight states, leading them to recommend that the disease is classified as "endemic." "Acknowledging the endemicity of Chagas disease in the United States is crucial for achieving global health goals," the authors wrote. The Centers for Disease Control and Prevention defines a disease as "endemic" when there is a "constant presence and/or usual prevalence" in a population within a specific geographic area -- in other words, the "baseline" level of disease within a community.

In November 2020, Alphabet-owned AI firm DeepMind announced that it had cracked one of biology's trickiest problems. For years the company had been working on an AI called AlphaFold that could predict the structure of proteins – a challenge that could prove pivotal for developing drugs and vaccines, and understanding diseases. When the results of the biennial protein-predicting challenge CASP were announced at the end of 2020, it was immediately clear that AlphaFold had swept the floor with the competition. John Moult, a computational biologist at the University of Maryland who co-founded the CASP competition, was both astonished and excited at AlphaFold's potential. "It was the first time a serious scientific problem had been solved by AI," he says.

New research from the University of Kansas shows machine learning is capable of identifying insects that spread the incurable disease called Chagas with high precision, based on ordinary digital photos. The idea is to give public health officials where Chagas is prevalent a new tool to stem the spread of the disease and eventually to offer identification services directly to the general public. Chagas is particularly nasty because most people who have it don't know they've been infected. But according to the Centers for Disease Control and Prevention, some 20 percent to 30 percent of the 8 million people with Chagas worldwide are struck at some later point with heart rhythm abnormalities that can bring on sudden death; dilated hearts that don't pump blood efficiently; or a dilated esophagus or colon. The disease is caused most often when triatomine bugs -- more commonly known as "kissing bugs" -- bite people and transmit the parasite Trypanosoma cruzi into their bloodstreams.